U.S. patent application number 11/976344 was filed with the patent office on 2008-03-06 for replicated multi-channel sensors for decucing ink thicknesses in color printing devices.
This patent application is currently assigned to Ecole Polytechnique Federale de Lausanne (EPFL). Invention is credited to Thomas Bugnon, Edoardo Charbon, Patrick Emmel, Roger D. Hersch.
Application Number | 20080055355 11/976344 |
Document ID | / |
Family ID | 34115775 |
Filed Date | 2008-03-06 |
United States Patent
Application |
20080055355 |
Kind Code |
A1 |
Hersch; Roger D. ; et
al. |
March 6, 2008 |
Replicated multi-channel sensors for decucing ink thicknesses in
color printing devices
Abstract
A method and computing system are proposed for deducing ink
thickness variations from solid-state multi-sensor measurements
performed online on a printing press or printer. The computed ink
thickness variations enable controlling the ink deposition and
therefore the color accuracy. Ink thickness variations are
expressed as ink thickness variation factors incorporated into an
ink thickness variation and sensor response enhanced spectral
prediction model. The ink thickness variation computing system
comprises multi-channel sensor devices (e.g. red, green, blue, near
infra-red), a processing module, and a computing system. The
multi-channel sensor devices are replicated over the width of the
print sheet. Preferably embodied by Single Photon Avalanche Diodes
(SPADs), due to their high-speed acquisition capabilities, they
provide responses according to the reflectance of small area
segments within a print sheet. The processing module accumulates
the digital sensor responses and forwards them to the computing
system, which deduces the ink thickness variations.
Inventors: |
Hersch; Roger D.;
(Epalinges, CH) ; Charbon; Edoardo; (Echandens,
CH) ; Bugnon; Thomas; (Lausanne, CH) ; Emmel;
Patrick; (Pratteln, CH) |
Correspondence
Address: |
Roger D. Hersch;EPFL-IC/LSP
Station 14
Lausanne
1015
CH
|
Assignee: |
Ecole Polytechnique Federale de
Lausanne (EPFL)
Lausanne
CH
|
Family ID: |
34115775 |
Appl. No.: |
11/976344 |
Filed: |
October 24, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10631743 |
Aug 1, 2003 |
|
|
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11976344 |
Oct 24, 2007 |
|
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Current U.S.
Class: |
347/19 |
Current CPC
Class: |
H04N 1/6033 20130101;
H04N 1/603 20130101; H04N 1/6025 20130101; G06K 2215/0094 20130101;
H04N 1/6097 20130101 |
Class at
Publication: |
347/019 |
International
Class: |
B41J 29/393 20060101
B41J029/393 |
Claims
1. A method for computing ink thickness variations for the control
of printing devices, the method being based on an ink thickness
variation and sensor response enhanced spectral prediction model,
said method comprising calibration steps and, during print
operation, online ink thickness variation computation steps, where
the calibration steps comprise the calculation of ink
transmittances, and where the ink thickness variation computation
steps comprise fitting of ink thickness variations by minimizing a
distance metric between predicted multi-channel sensor responses
and acquired multi-channel sensor responses, said predicted
multi-channel sensor responses being computed according to the ink
thickness variation and sensor response enhanced spectral
prediction model, and said. acquired multi-channel sensor responses
being generated by light reflected on a print sheet.
2. The method of claim 1, where the print sheet is moving and the
multi-channel sensor devices, due to their high-speed acquisition
capabilities, provide responses according to the reflectance of
small area segments within the print sheet, and where the
calibration steps also comprise, in order to account for ink
spreading, fitting of effective surface coverage curves mapping
nominal to effective surface coverages of single ink halftones in
different superposition conditions.
3. The method of claim 1, comprising also during said print
operation online calibration steps, said online calibration steps
comprising a step of fitting effective surface coverage curves
mapping nominal to effective surface coverages of single ink
halftones in different superposition conditions by acquisition of
sensor responses from polychromatic halftones.
4. The method of claim 1, where the thickness variation and sensor
response enhanced spectral prediction model comprises as solid
colorant transmittance of at least two superposed solid inks the
transmittance of each of the superposed inks raised to the power of
a product of variables, one variable being the superposition
condition dependent ink thickness and the other variable being the
ink thickness variation factor.
5. The method of claim 1, where the inks are the cyan, magenta,
yellow, and black inks and where the thickness variation and sensor
response enhanced spectral prediction model operates simultaneously
in the visible and near infra-red wavelength range domain.
6. The method of claim 1, where the ink thickness variation
computation steps also comprise the step of online recording of
reference thickness variations and where the computed ink thickness
variations are ink thickness variations normalized in respect to
the reference ink thickness variations.
7. The method of claim 1, where, in addition to the calibration and
recalibration steps, the step of acquiring during print operation
reference sensor responses from a reference print under reference
settings and of deducing corresponding reference effective surface
coverages is performed, where the sensor responses are predicted
with the deduced reference effective surface coverages, and where
the computed ink thickness variations represent ink thickness
variations in respect to the reference print.
8. The method of claim 1, where said multi-channel sensor devices
are based on single photon avalanche diodes (SPADs) which capture
during print operation light reflected by said small area segments
within the print page.
9. The method of claim 8, where light emitting diodes (LEDs) emit
light that is directed towards the print sheet, where part of said
light penetrates the print sheet, is reflected by the sheet's
substrate and captured by said SPAD sensor devices.
10. An ink thickness variation computing system for the control of
printers, respectively printing presses operable for the online
computation of ink thickness variations during print operation,
said ink thickness variation computing system comprising
multi-channel sensor devices, a processing module, and a computing
system, where the multi-channel sensor devices respond at different
spectral sensibility ranges within the visible and near infra-red
wavelength range, where the multi-channel sensor devices, due to
their high-speed acquisition capabilities, provide responses
according to the reflectance of small area segments within a print
sheet, where the processing module receives the responses from said
multi-channel sensor devices and forwards them to the computing
system, which according to an ink thickness variation and sensor
response enhanced spectral prediction model deduces said ink
thickness variations.
11. The ink thickness variation computing system of claim 10, where
said multi-channel sensor devices are based on single photon
avalanche diodes (SPADs) which capture light reflected by said
small area segments within the print sheet.
12. The ink thickness variation computing system of claim 11, where
single photon avalanche diodes photon count acquisition times range
between 200 nanoseconds and 10 milliseconds.
13. The ink thickness variation computing system of claim 11, where
the processing module comprises a multiplexer, a fast logic, a
pulse counter and a microcontroller, where the multiplexer is
operable for selecting the SPAD whose pulses are counted, where the
pulse counter is operable for counting the pulses received from the
SPADs and where the microcontroller is operable for storing the
resulting pulse count and for transmitting it to the computing
system.
14. The ink thickness variation computing system of claim 11, where
light emitting diodes (LEDs) emit light that is directed towards
the print sheet, where part of said light penetrates the print
sheet, is reflected by the sheet's substrate and captured by said
SPAD sensor devices.
15. The ink thickness variation computing system of claim 11, where
white light is filtered by filters having different spectral
sensibilities within the visible and near infra-red wavelength
range and directed towards the print sheet, where part of said
filtered light penetrates the print sheet, is reflected by the
print sheet's substrate and captured by said SPAD sensor
devices.
16. The ink thickness variation computing system of claim 11, where
white light illuminates an area segment of said print sheet, is
reflected by said area segment, is filtered by filters having
different spectral sensibilities within the visible and near
infra-red wavelength range and is captured by said SPAD sensor
devices.
17. The ink thickness variation computing system of claim 11, where
one input and one output polarizing filters discard part of said
light that is specularly reflected at the surface of said moving
print sheet.
18. The ink thickness variation computing system of claim 10
forming together with an additional print actuation parameter
driving module an online ink regulation system operable for
controlling according to the deduced ink thickness variations the
amount of ink deposited onto a substrate.
19. The ink thickness variation computing system of claim 18, where
controlling the amount of ink deposited onto a substrate is
performed in case of a printed press by ink feed, in case of an ink
jet printer by a function selected from the set of droplet ejection
control and droplet count, in case of an electrophotographic
printer by a function selected from the set of toner transfer and
fusing and in case of a thermal transfer, respectively dye
sublimation printer, by controlling head element temperature
profiles.
20. The ink thickness variation computing system of claim 10, where
the inks are the cyan, magenta, yellow, and black inks and where
said thickness variation and sensor response enhanced spectral
prediction model operates in the visible and near infra-red
wavelength range.
21. The ink thickness variation computing system of claim 10, where
said computing system also performs an online refined calibration
of said thickness variation and sensor response enhanced spectral
prediction model by deducing paper reflectances of said print
sheets and by fitting according to the multi-channel sensor
responses effective surface coverage curves mapping nominal to
effective surface coverages of single ink halftones in different
superposition conditions.
22. The ink thickness variation computing system of claim 10, where
said computing system also records reference thickness variations
and where the computed ink thickness variations are ink thickness
variations normalized in respect to the reference ink thickness
variations.
23. The ink thickness variation computing system of claim 10, where
said computing system also records reference sensor responses from
a reference print under reference settings, deduces corresponding
reference effective surface coverages, and predicts sensor
responses with the deduced reference effective surface coverages
and where the computed ink thickness variations represent ink
thickness variations in respect to the reference print.
Description
[0001] The present patent application is a continuation-in-part of
U.S. patent application Ser. No. 10/631743, Prediction model for
color separation, calibration and control of printers, inventors R.
D. Hersch, P. Emmel, F. Collaud, filed Aug. 1, 2003.
BACKGROUND OF THE INVENTION
[0002] The present invention relates to the field of color printing
and more specifically to the control of color printer actuation
parameters. It discloses a new concept of non-expensive replicated
illuminating and sensor devices placed on the printer in face of
the moving paper sheets as well as a spectral prediction model
extension adapted to the proposed set of illuminating and sensor
devices.
[0003] Color control in printing presses is desirable in order to
ensure that effectively printed colors correspond to the desired
colors, i.e. the colors expected by the prepress color separation
stage. Color consistency is desirable both across consecutive
sheets of a multi-sheet print job and also from print job to print
job.
[0004] In the prior art, densitometers were often used to control
the amount of ink of single ink printed patches. The densitometer
measures the optical density, which is an approximate measure of
the ink thickness. In the prior art, the control of print actuation
parameters affecting the printed output such as the ink thickness
is generally performed by an operator or by an apparatus measuring
the density of solid ink or of halftone ink patches, see U.S. Pat.
No. 4,852,485 (Method of operating an autotypical color offset
machine, Inventor F. Brunner, issued Aug. 1, 1989). Special patches
are usually integrated along the borders of printed sheets and
serve as a means to measure their density. These special patches
need however to be subsequently cut out.
[0005] U.S. Pat. No. 6,230,622 (Image data-oriented printing
machine and method of operating the same, to P. Dilling, issued May
15, 2001) teaches a method for operating a printing machine with an
expert system which determines the effect of the interaction of a
large number of print parameters and acts on some of these
parameters in order to reach a high print quality. The proposed
method relies only on density measurements. Due to the large number
of parameters which need to be taken into account, this solution
seems complex and costly.
[0006] U.S. Pat. No. 5,903,712, Ink separation device for printing
press ink feed control, to X. X. Wang, and R. J. Nemeth, filed Oct.
5, 1995, issued May 11, 1999, teaches an ink separation device or
process where red, green, blue and infra-red scalar reflection
values within a printed sheet are measured and converted into cyan,
magenta, and yellow dot size values according to a previously
initialized transfer function. By comparing the so-obtained dot
size values with reference dot size values, a dot size ratio is
derived to adjust the ink feed rate of the press. The transfer
function is a multi-variable polynomial of order 6. It comprises
about 80 different coefficients which need to be regressed for each
combination of paper and ink set. A specially printed test form
with hundreds of patches is needed to enable these regressions. In
that invention, the relationship between 4 channel sensor responses
and the ink dot sizes is considered to be unknown, i.e. a black box
whose behavior is modeled by the multi-variable high-order
polynomial. Such high order polynomials are known to oscillate
between the known values of input/output variables and therefore do
not always provide a correct mapping between sensor input variables
and ink dot size output variables. In contrast, in our invention,
we rely on a physically-based spectral prediction model describing
the interaction of light, ink halftones and paper, as well as the
ink spreading phenomenon. The model we propose is therefore robust
and each of its elements (paper reflectance, ink transmittances,
effective surface coverages) is separately characterized from
spectral reflectance or from multi-channel sensor response
measurements.
[0007] U.S. Pat. No. 6,684,780, Ink control in printing press,
filed Jan. 21 2003, issued Feb. 3, 2004, to Y. Shiraishi, teaches a
method for ink key aperture control by using a color difference
between an original image and an RGB CCD camera image of the print.
By using a conversion table, color differences are converted into
corresponding desired density corrections, which, through a second
conversion table, are converted into ink key aperture correction
values. Since these two conversion tables are deduced from
experiments which may have been performed under different printing
conditions (temperature, settings of the press, etc.), the control
of the ink aperture is not precisely adapted to the current
operating conditions of the press. In addition, experience shows
that it is very difficult to control the ink feed of 4 inks
(c,m,y,k) with a 3-sensor system only. Experience also shows that
using a spectral prediction model incorporating explicitly the ink
thickness terms provides more robustness than a pure colorimetric
approach.
[0008] U.S. Pat. No. 6,611,357, Method of stipulating values for
use in the control of a printing machine, to K. Wendt and P.
Schramm, filed Jan. 26, 2001, issued Aug. 26, 2003, teaches a
method for controlling printers by determining according to the
surface coverages of individual inks within an original image
element the predicted (desired) color spectrum, and achieving that
color spectrum by varying the actual area coverages of the
individual inks by multiplicative factors deduced from spectra
predictions. Spectra are predicted according to a weighted average
of the reflection spectra of the inks, the weights being determined
by the respective surface coverages of the inks. The reflectance
spectra of ink superpositions are not considered. It is known in
the art that no accurate spectral or color predictions can be made
without considering explicitly the reflectance of superposed
inks.
[0009] U.S. Pat. No. 6,679,169, Ink control model for controlling
the ink feed in a machine which processes printing substrates,
filed Oct. 24, 2002, issued Jan. 20, 2004, to Anweiler, Gateaud,
Hauck and Mayer teaches a method for controlling the ink feed in a
printing press by deducing the ink feed rate from stored physical
properties of inks and paper. That method does not consider
controlling the ink feed rate according to sensor responses of
illuminated polychromatic halftones.
[0010] U.S. Pat. No. 4,975,852, Process and apparatus for the ink
control of a printing machine, to G. Keller and H. Kipphan, filed
Jan. 5, 1989, issued Dec. 4, 1990, U.S. Pat. No. 5,182,721, Process
and apparatus for controlling the inking process in a printing
machine, to H. Kipphan, G. Loffler, G. Keller and H. Ott, filed
Sep. 28, 1990, issued Jan. 26, 1993, and U.S. Pat. No. 6,041,708,
Process and apparatus for controlling the inking process in a
printing machine, to H. Kipphan, G. Loffler, G. Keller and H. Ott,
filed Aug. 22, 1994, issued Mar. 28, 2000 teach methods to derive
ink layer thickness variations from spectral reflectance
differences of specially printed test patches by converting these
differences to CIELAB differences and by multiplying these
differences by the inverse of a matrix whose components are
derivatives of the CIELAB components in respect to the cyan,
magenta and yellow ink thicknesses. The elements of the matrix are
dependent on the area coverage of the inks and need therefore to be
calibrated for each considered test patch.
[0011] U.S. Pat. No. 6,564,714, Spectral color control method, to
D. Brydges and E. Tobiason, Jul. 26, 2001, issued May 20, 2003,
teaches a method to derive ink layer thickness correction values
from spectral reflectance differences of specially printed test
patches. Ink layer thickness differences are obtained by
multiplying the spectral reflectance difference vector with a
correction matrix expressing the derivatives of the ink layer
thicknesses in respect to each of the monochromatic reflectance
values. The elements of the correction matrix are dependent on the
area coverage of the inks and need therefore be calibrated for each
test patch. In contrast, our invention provides a single
computation model for deriving ink thickness variations from
halftone prints. It does not require halftone area coverage
dependent calibrations to be performed.
[0012] U.S. Pat. No. 7,077,064, Methods for measurement and control
of ink concentration and film thickness, to D. Rich, filed Apr. 19,
2005, issued Jul. 18, 2006, teaches a method to deduce ink
thickness as well as ink concentration from red, green and blue
responses of a camera, using a variant of the Kubelka-Munk model.
It is known that the Kubelka-Munk model only works on uniformly
diffuse layers and is therefore not applicable to halftones. In
contrast, our invention deduces ink thickness variations from
halftones.
[0013] US parent patent application Ser. No. 10/631743 (Prediction
model for color separation, calibration and control of printers,
inventors R. D. Hersch, P. Emmel, F. Collaud, filed Aug. 1, 2003)
teaches a method to deduce the ink thicknesses for a color patch
printed with 2, 3 or 4 inks. The method works for deducing the ink
thicknesses on single ink patches, on two ink patches and on 3 ink
patches. But due to the uncertainty between joint variations in the
ink thicknesses of cyan, magenta, and yellow and a variation in
thickness of black, the method does not work well for the set of
cyan, magenta, yellow and black inks. In addition, that method does
not teach how to calibrate the prediction model with halftones that
are an integral part of a printed document page delivered to a
customer. The present invention improves upon that application by
introducing a 4.sup.th infra-red sensor to separate ink thickness
variations of black from joint ink thickness variations of cyan,
magenta and yellow. In addition, the present invention clearly
separates the calibration process into an offline calibration with
spectral measurement devices on specially printed patches and an
online calibration with multi-sensor responses on halftones located
within a normal printed page.
[0014] U.S. patent application Ser. No. 10/698667 (Inks Thickness
Consistency in Digital Printing Presses, to Staelin et al., filed
Oct. 31, 2003) teaches a model for estimating ink thickness control
parameters such as the developer voltage in case of an
electrographic printer. This model takes as input values
measurements of the internal state of a digital printing press as
well as of the densities of monochrome patches. This patent
application does neither teach how to obtain ink thickness control
parameters from polychromatic halftone patches nor from halftones
being part of the actual printed document pages.
[0015] U.S. Pat. No. 7,000,544, (Measurement and regulation of
inking in web printing, to Riepenhoff, filed 1.sup.st Jul. 2002)
teaches a process for measuring the mean spectrum integrated over a
stripe of the printed sheet. It also teaches a device for
regulating the ink density by predicting the mean reflection
spectrum along a stripe thanks to a correspondence function between
image data located along the stripe and the resulting reflection
spectrum. That correspondence function does not incorporate an
explicit ink thickness variable, nor does it make the distinction
between nominal surface coverages and effective surface coverages.
It therefore does not account for the ink spreading phenomenon.
[0016] U.S. Pat. No. 7,252,360, Ink thickness variations for the
control of color printers, filed 25.sup.th Oct. 2005, issued
7.sup.th of Aug. 2007, to R. D. Hersch (also inventor in the
present patent application), P. Amrhyn and M. Riepenhoff, teaches a
spectral prediction model for deducing ink thickness variations
working with a single head spectrophotometer located on the running
printing press, for acquiring halftones or mean reflection spectra
over stripes (spectral acquisition averaged over the length of a
print). The present invention improves upon U.S. Pat. No.
7,252,360, by replacing the single moving head spectrophotometer by
a sensing system formed by non-moving multiple-channel solid-state
high-speed acquisition sensor devices replicated over the width of
the printer (printed sheet width). Solid state sensor devices,
especially when embodied by Single Photon Avalanche Diodes, provide
a cheaper solution, compared with a moving head spectrophotometer.
In addition, in the present invention, the high-speed sensor
response enables sensing small halftone area segments which do not
incorporate much paper white and exhibit therefore less noise.
Furthermore, the new online calibration steps are performed on the
running press and do not require specially printed uniform color
patches or control stripes.
[0017] The present disclosure provides a robust means of deducing
online and in real time ink thickness or ink volume variations of
cyan, magenta, yellow and black on a running printing press or
color printer, without needing at print time specific solid or
halftone patches within the printed sheet. In addition, due to an
optional online calibration, the deduced ink thickness variations
are accurate in respect to the current printing device operating
conditions (temperature, settings of the printing device, etc. . .
. ).
SUMMARY
[0018] The present invention proposes a method and a computing
system for deducing ink thickness variations from multi-channel
sensor responses acquired online during print operation of a
printing press or a printer. Acquiring the ink thickness variations
online and in real-time enables regulating the ink deposition
process during normal print operation. Real-time online control of
the ink deposition process enables keeping a high color accuracy
from print sheet to print sheet and from print job to print job.
This is especially important if the ink deposition process is not
stable when working in open loop mode.
[0019] Ink thickness variations are expressed as ink thickness
variation factors incorporated into an ink thickness variation and
sensor response enhanced spectral prediction model. The method for
computing ink thickness variations comprises both calibration and
ink thickness variation computation steps. The calibration steps
comprise the measurement and adjustment of paper reflectance,
possibly the calculation of internal paper reflectance, the
calculation of ink transmittances from measured reflectances, the
computation of scalar ink thicknesses of solid superposed inks and,
in order to account for ink spreading, the computation of effective
surface coverages of single ink halftones in different
superposition conditions. By interpolation, we obtain the effective
surface coverage curves mapping nominal to effective surface
coverages of single ink halftones in different superposition
conditions. The calibration steps can be divided into offline and
online calibration steps. The offline calibration steps require
specially printed patches such as solid ink and solid ink
superposition patches on which spectral reflectance measurements
are performed. From these spectral reflectance measurements, the
internal reflectance of paper and the transmittance of the inks are
obtained. The optional online calibration steps improve the
calibration in case of changes in the printer operating conditions,
e.g. a change in temperature, a change of paper, or a new set of
inks which differs from the previous set. Online calibration steps
are performed only with multi-channel sensor responses from printed
area segments located within the printed sheet. They may comprise
the recalibration of the paper reflectance, and possibly the
calibration or recalibration of the effective surface coverage
curves. They may also comprise deducing reference thickness
variations which are then used to compute thickness variations
normalized in respect to these reference thickness variations.
[0020] In respect to the ink thickness variation computation steps,
the thickness variation and sensor response enhanced spectral
prediction model comprises as solid colorant transmittance of two
or more superposed solid inks the transmittance of each of the
contributing superposed ink raised to the power of a product of two
variables, one variable being the superposition condition dependent
ink thickness and the other variable being the ink thickness
variation factor. The ink thickness variations are fitted by
minimizing a distance metric between predicted multi-channel sensor
responses and acquired multi-channel sensor responses, the
predicted multi-channel sensor responses being computed according
to the ink thickness variation and sensor response enhanced
spectral prediction model.
[0021] With one of the multi-sensor channels also operating in the
near infra-red region, for black inks absorbing in the near
infra-red region, the ambiguity between ink thickness variations of
the black ink and joint ink thickness variations of the cyan,
magenta, and yellow inks is resolved.
[0022] In case that the area sensed by the multi-sensor devices
comprises significantly different colors, the obtained sensor
response is a mean value over sensor responses across several
nearly uniform color sub-areas. Corresponding predicted sensor
responses can be computed by predicting sub-areas sensor responses
and by performing a weighted average over these sub-area sensor
responses, the weights corresponding to the respective surface
coverages of the sub-areas.
[0023] If the nominal surface coverages of the halftone area
segment on which thickness variations are to be performed are
unknown, it is possible, in addition to the calibration of the
transmittances and the thicknesses of the inks, to measure sensor
responses from a reference print under reference settings and to
deduce with the thickness enhanced spectral prediction model the
corresponding reference effective surface coverages. The sensor
responses are then predicted with the deduced reference effective
surface coverages. Ink thickness variations are computed by
minimizing a distance metric between predicted sensor responses and
measured sensor responses. The computed ink thickness variations
represent ink thickness variations in respect to the reference
print.
[0024] The disclosed ink thickness variation computing system
computes ink thickness variations online and in real time. It
comprises multi-channel sensor devices, a processing module, and a
computing system. The multi-channel sensor devices are replicated
over the width of the print sheet. The multi-channel sensor devices
respond at different spectral sensibility ranges within the visible
and near infra-red wavelength range. The multi-channel sensor
devices, due to their high-speed acquisition capabilities, provide
responses according to the reflectance of small areas within a
print sheet. The processing module accumulates the digital sensor
responses and forwards them to the computing system, which
according to an ink thickness variation and sensor response
enhanced spectral prediction model deduces the ink thickness
variations.
[0025] In a preferred embodiment, the sensor devices are Single
Photon Avalanche Diodes, which create a pulse upon arrival of a
photon. With pulse dead times in the order of 20 ns to 50 ns it is
possible to have a maximal photon count between 20000 and 50000
pulses per millisecond. This allows to have both low sensor
acquisition times (e.g. 0.2 ms to 1 ms) and a high signal to noise
ratio. At a printer speed of 10 m/s, the area segment length
passing underneath a sensor has a corresponding length of 2 mm to
10 mm. In a possible layout, there may be one colored, respectively
infra-red, LED in front of each sensor with the light of the LED
being directed towards the print and being reflected into the
corresponding sensor. The colored and infra-red LED's may also be
replaced by a white LED followed by a corresponding colored,
respectively infra-red, filter. To avoid specular reflection, the
incident light may be oriented towards the print at 45 degrees and
read out at zero degrees. Alternately, to discard specular
reflections, it is also possible to have a first polarizer on the
incident light path and second polarizer turned by 90 degrees in
respect to first one on the reflected light path.
[0026] In a further possible layout of the illumination and sensing
system, the sensing system may be formed by the multi-channel
sensors located within a single integrated circuit. The
illumination may be produced by a white LED whose light is directed
towards the print. The reflected light is for example filtered by
specific filters each located in front of its respective
sensor.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] FIG. 1 shows a schematic view of an ink thickness variation
computation model embodiment, where ink thickness variations
dr.sub.C, dr.sub.M, dr.sub.Y are deduced from known nominal surface
coverages of cyan (c), magenta (m), and yellow (y) inks and from
sensor responses q.sub.r, q.sub.g, q.sub.b;
[0028] FIG. 2 illustrates a generalization of the ink thickness
variation computation model of FIG. 1, for a set of 4 inks with
nominal input surface coverages c.sub.1, c.sub.2, c.sub.3, c.sub.4,
4-channel sensor responses q.sub..alpha., q.sub..beta.,
q.sub..gamma., q.sub..delta. and output ink thickness variations
dr.sub.1, dr.sub.2, dr.sub.3, dr.sub.4;
[0029] FIG. 3A shows a sensing system comprising 4-channel
illuminating/sensor devices replicated over the width of the
printed sheet and their corresponding input and output signals;
[0030] FIG. 3B shows a sensor processing module comprising a
multiplexer, a counter, fast logic (FL) and a microcontroller
(.mu.C);
[0031] FIG. 4A shows a 4 channel illuminating/sensing device set
and its corresponding input and output signals;
[0032] FIG. 4B shows one integrated circuit comprising a 4 channel
sensing device set, pulse counters and a multiplexer;
[0033] FIG. 5A shows an embodiment of a single illuminating/sensing
device with an incident angle of 45.degree. and a light capture
angle of 0.degree.;
[0034] FIG. 5B shows a further embodiment of a single
illuminating/sensing device comprising a light-emitting diode
(LED), a polarizing filter 514 on the incident light path, a beam
splitter 510 and a second polarizing filter 511 on the reflected
light path;
[0035] FIG. 5C shows a further embodiment with a white diffuse
illumination 534 illuminating the print sheet 537 and a sensing
device 533 whose focalizing lens 531 is coated with a filtering
substance 530.
[0036] FIG. 6 shows a possible embodiment of blue, green, red and
infra-red sensor sensibilities;
[0037] FIG. 7 shows an ink thickness variation computing system
comprising a sensing system 708, a computing system 701 and a print
actuation parameter driving module 709; and
[0038] FIGS. 8A and 8B illustrate respectively examples of deduced
normalized ink thickness variations of the magenta, respectively
the black ink in many different print trials, where the ink feed of
one or of several inks has been increased or decreased.
DETAILED DESCRIPTION OF THE INVENTION
[0039] The present invention proposes models, a computing system as
well as methods for deducing ink thickness variations from sensor
responses obtained on a printer or printing press, online and in
real-time. The computed ink thickness variations enable controlling
the ink deposition and therefore the color accuracy, in the case of
high-speed printing presses, of network printers and desktop
printers. The ink thickness variations can be directly used for the
real-time control of the print actuation parameters which influence
the ink deposition, such as the ink feed in the case of an offset
press.
[0040] The proposed method and computing system rely on a spectral
prediction model incorporating as input parameters the responses
from multi-channel sensors, as internal parameters the ink
thicknesses and as output parameters ink thickness variation
factors. Hereinafter, such a model is called "thickness variation
and sensor response enhanced spectral prediction model". Ink
thickness variations are deduced by an "ink thickness variation
computation model". When the ink thickness variation computation
model is embodied by a computing system, it becomes an "ink
thickness variation computing system". By deriving from the
thickness variation computation model a series of processing steps,
we obtain a thickness variation prediction method.
[0041] In the present invention, unknown variables are fitted by
minimizing a distance metric (also called difference metric)
between a measured reflection spectrum, respectively sensor
responses, and a reflection spectrum, respectively sensor responses
predicted according to a spectral reflectance prediction model. The
preferred distance metric is the sum of square differences between
the corresponding measured and predicted reflection density
spectra, respectively sensor density responses, with reflection
density spectra, respectively sensor density responses, being
computed according to formula (2a), respectively (2b). Minimizing a
distance metric can be performed, for example with a software
package such as Matlab or with a program implementing Powell's
function minimization method (see W. H. Press, B. P. Flannery, S.
A. Teukolsky, W. T. Fetterling, Numerical Recipes, Cambridge
University Press, 1st edition, 1988, section 10.5, pp.
309-317).
[0042] The present invention deals with deducing ink thickness
variations from sensor responses of illuminated halftones with
color inks. The substrate on which the inks are printed is paper in
the general case. But in other cases, the substrate can be another
diffusely reflecting substrate such as a polymer. In the present
invention, the term "paper" is meant in a generic sense and
designates any diffusely reflecting substrate.
[0043] Once printed, the physical size of the printed dot generally
increases, partly due to the interaction between the ink and the
paper, and partly due to the interaction between successively
printed ink layers. This phenomenon is called physical (or
mechanical) dot gain or ink spreading. Therefore, "nominal surface
coverages" (or simply "nominal coverages") are initially specified
amounts of inks and "effective surface coverages" (or simply
"effective coverages") are physical surface coverages inferred from
the spectral or sensor response measurements of the printed patches
according to the considered spectral prediction model.
[0044] Halftones which are printed with multiple, partly superposed
inks are called polychromatic halftones. A solid ink patch is a
patch printed with 100% surface coverage. A halftone is a printed
area, e.g. a small rectangle within a printed sheet, where at least
one ink layer is printed in halftone. Halftones may form an
integral part of a printed document page delivered to a customer,
i.e. they may be located within color images, within gray or
colored bars separating different parts of a printed document page,
or they may form the gray or colored background of
advertisements.
[0045] A calibration halftone patch is a uniform color patch where
one ink is printed as a halftone at a specified nominal surface
coverage value, for example 25%, 50% or 75%. This halftone may be
printed alone on paper or printed in superposition with other solid
inks. Note that since one of the goals of the present invention is
to avoid printing special control stripes or halftone patches at
the borders of a printed document page, the disclosed online
calibration does not require calibration halftone patches.
[0046] A printed area segment may be located within a printed page.
The printed area segment is a rectangle whose length corresponds to
a small displacement of a print page location under a sensor. The
width of the rectangle corresponds to the width of the sensor.
[0047] The considered inks are usually the standard cyan, magenta,
yellow and black inks. But the disclosed ink thickness variation
computation model may also be applied in a straightforward manner
to inks of other colors. For example, the set of inks may comprise
the standard cyan, magenta and yellow inks plus one or several
additional inks such as orange, red, green and blue. The term "ink"
is used in a generic sense: It may comprise any colored matter that
can be transferred onto specific locations of a substrate (e.g.
offset inks, ink-jet inks, toner particles, liquid toner, dye
sublimation colorants, etc. . . . ).
[0048] In the present invention, the multi-channel sensors
replicated over the width of the printed sheet comprise for each
channel an illuminating and sensor device, also called
"illuminating/sensor device". In the case of multiple channels, we
have a "set of illuminating/sensor devices" or simply an
"illuminating/sensor set". When such an illuminating/sensor set is
replicated over the width the print, it is called
"illuminating/sensor array". An illuminating/sensor set may
comprise one illuminating device and several sensors.
[0049] Throughout the application the expressions "printing
device", "printer" and "printing press" are used interchangeably,
i.e. the disclosure with respect to one is equally applicable with
respect to the other. The invention is advantageous for computing
ink thickness variations or equivalently, ink colorant
concentration variations by a computing system which regulates the
print actuation parameters controlling the amount of deposited
colorant substance or ink, such as the ink feed (ink volume), the
ink thickness, or the ink colorant concentration.
[0050] The present invention also enables controlling the ink
deposition in printers, such as electro-photographic printers,
ink-jet printers, solid-tone printers, liquid-toner printers, dye
sublimation printers and thermal transfer printers. In such
printers, there is often the possibility of varying the size of the
individual printed dot. The number of available dot sizes depends
on the printer technology, and may range from 3 to 255 different
dot sizes. Increasing, respectively decreasing, the amount of ink
can also be achieved by increasing, respectively decreasing, the
individual printed dot size.
[0051] For a printing press (e.g. a web-offset press), deducing ink
thickness variations from sensor responses enables the automatic
regulation of the thickness (or volume) of the deposited inks by
acting on the print actuation parameters such as the ink feed. For
a digital printer hooked onto a computer network or for a desktop
printer, deduction of ink thickness variations enables adjusting
the printer settings by acting on the print actuation parameters,
such as the droplet ejection mechanism in the case of an ink-jet
printer, the electronic charge and discharge mechanism as well as
possibly the fusing mechanism in the case of an electrophotographic
printer, and the head element temperature profiles in the case of
thermal transfer or dye sublimation printers.
[0052] Deducing the thickness variations of the inks from sensor
responses of illuminated halftones is achieved thanks to an
enhanced spectral reflectance prediction model which has an
explicit representation of the wavelength-dependent ink
transmittances, of the wavelength-dependent reflectance of paper,
of the wavelength-dependent sensibilities of each of the
multi-channel sensor devices, of wavelength-independent ink
thicknesses, and of wavelength-independent ink thickness
variations. This ink thickness and sensor response enhanced
spectral reflectance prediction model takes into account ink
spreading, i.e. the mapping from nominal to effective dot surface
coverages under different ink superposition conditions.
[0053] The embodiment of the disclosed ink thickness variation
computation model presented here uses as base spectral prediction
model either the Clapper-Yule spectral reflection prediction model
or the Yule-Nielsen modified spectral Neugebauer model, see D. R.
Wyble, R. S. Berns, A Critical Review of Spectral Models Applied to
Binary Color Printing, Journal of Color Research and Application,
Vol. 25, No. 1, 4-19, 2000, hereinafter referenced as [Wyble and
Berns 2000].
[0054] The Clapper-Yule model, see F. R. Clapper, J. A. C Yule,
"The effect of multiple internal reflections on the densities of
halftone prints on paper", Journal of the Optical Society of
America, Vol. 43, 1953, 600-603, hereinafter referenced as
[Clapper53], takes simultaneously into account halftone patterns
and multiple internal reflections occurring at the interface
between the paper and the air and assumes a relatively high screen
frequency. In a recent extension, the Clapper-Yule model has been
combined with a Saunderson corrected spectral Neugebauer model
which gives assumes a low screen frequency, see R. D. Hersch and
al, "Spectral reflection and dot surface prediction models for
color halftone prints", R. D. Hersch, et. al., Journal of
Electronic Imaging, Vol. 14, No. 3, August 2005, pp. 33001-12,
incorporated in the present disclosure by reference, hereinafter
referenced as [Hersch05A]. The weighting factor b indicates the
relative weight of the Saunderson corrected Neugebauer model
component. For four ink prints, this composed Clapper-Yule
Saunderson corrected Neugebauer spectral reflection prediction
model, hereinafter referenced as "CYSN spectral prediction model"
is formulated as follows: R .function. ( .lamda. ) = K * r s + ( 1
- r s ) * r g .function. ( .lamda. ) * ( 1 - r i ) [ b .times. j
.times. = .times. 1 .times. 16 .times. .times. .times. a .times. j
* .times. .times. t .times. j .times. ( .lamda. ) 2 .times. 1
.times. - .times. r .times. g .times. ( .lamda. ) * .times. r
.times. i * .times. t .times. j .times. 2 .times. ( .lamda. )
.times. + .times. ( 1 .times. - .times. b ) .times. .times. (
.times. j .times. = .times. 1 .times. 16 .times. .times. a .times.
j * .times. t .times. j .times. ( .lamda. ) ) 2 .times. 1 .times. -
.times. r .times. g .times. ( .lamda. ) * .times. r .times. i *
.times. j .times. = .times. 1 .times. 16 .times. .times. a .times.
j * .times. t .times. j .times. 2 .times. ( .lamda. ) ] ( 1 .times.
a ) ##EQU1## where K is the fraction of specular reflected light
reaching the spectrophotometer (for a 45/0 degrees measuring
geometry, K=0), r.sub.s is the surface reflection at the air paper
coating interface, r.sub.g is the paper substrate reflectance,
r.sub.i is the internal Fresnel reflection factor obtained by
integrating the Fresnel reflection factor over all orientations,
a.sub.j represents the fractional surface coverage of a colorant j,
t.sub.j represents the transmittance of a colorant j, and
R(.lamda.) is the predicted reflection spectrum. The b weighting
factor is fitted for a giving printing process by minimizing on a
subset of halftones a difference metric between predicted
reflection spectrum and measured reflection spectrum. At a screen
frequency (screen ruling) above 120 lpi (lines per inch),
experience shows that b tends to zero, i.e. the Clapper-Yule
component of the model is sufficient and provides a high enough
accuracy.
[0055] Instead of using the Clapper-Yule Saunderson corrected
Neugebauer model (CYSN) as base spectral reflectance model, one may
equally well use the Yule-Nielsen modified spectral Neugebauer
model (YNSN) extended with effective coverages in different
superposition conditions (see R. D Hersch and al, "Improving the
Yule-Nielsen modified spectral Neugebauer model by dot surface
coverages depending on the ink superposition conditions",
IS&T/SPIE Electronic Imaging Symposium, Conf. Imaging X:
Processing, Hardcopy and Applications, January 2005, SPIE Vol.
5667, 434-445, incorporated by reference) where reflection spectra
of the colorants R.sub.j are expressed by colorant transmittance
spectra t.sub.i and the paper reflectance R.sub.g, as expressed in
Eq. (1b).
[0056] For 4 inks, the corresponding Yule-Nielsen modified Spectral
Neugebauer reflectance is R .function. ( .lamda. ) = ( j = 1 16
.times. a j R j .function. ( .lamda. ) 1 n ) n ; .times. .times.
with ( 1 .times. b ) R j .function. ( .lamda. ) = t j .function. (
.lamda. ) 2 * R g .function. ( .lamda. ) ( 1 .times. c ) ##EQU2##
where the Yule-Nielsen scalar n-value is fitted for a giving
printing process by minimizing on a subset of halftones a
difference metric between predicted reflection spectrum and
measured reflection spectrum. Reflectances R.sub.j (.lamda.) are
measured reflectances of paper (R.sub.g), of solid inks and of
solid ink superpositions printed on paper. In the YNSN model, there
is no need to derive an internal paper reflectance from a measured
paper reflectance.
[0057] The reflection density spectrum D(.lamda.) is deduced from
the reflectance R(.lamda.) according to the following well known
formula D(.lamda.)=-log.sub.10(R(.lamda.)) (2a)
[0058] Equations (1a), (1b) define respectively two different
embodiments of base spectral prediction models. For each
embodiment, either the reflection spectrum R(.lamda.) or the
reflection density spectrum D(.lamda.) can be predicted.
[0059] In the present invention, instead of a spectral acquisition
device (spectrophotometer), we consider for online use sensor
acquisition devices such as blue, green, red and near-infra-red
sensors. The CYSN or YNSN spectral prediction models given above
can be extended to predict the response of the sensors.
[0060] In the case of 4 .alpha., .beta., .gamma., .delta.
illuminating and sensor devices having respective illuminant
spectra I.sub..alpha., I.sub..beta., I.sub..gamma., I.sub..delta.,
and spectral sensitivities S.sub..alpha., S.sub..beta.,
S.sub..gamma., S.sub..delta., the responses of the .alpha., .beta.,
.gamma., .delta. sensors expressing the amount of light reflected
by the sample of spectral reflectance R can be described by
Equations (3). In these equations, all spectral values are discrete
values, distributed across the wavelength range of interest, e.g.
between 380 nm and preferably 900 nm for the visible wavelength and
near infra-red wavelength range. The differences in sensibility
between channels .alpha., .beta., .gamma., .delta. may be due to
different illuminations (e.g. blue, green, red, and infra-red
illuminations expressed by I.sub..alpha., I.sub..beta.,
I.sub..gamma., I.sub..delta.) or due to filters located in the
pathway of the light to the sensors, expressed as combined filter
and sensor sensibilities S.sub..alpha., S.sub..beta.,
S.sub..gamma., S.sub..delta.. q .alpha. = i .times. S .alpha.
.times. .times. i .times. R i .times. I .alpha. .times. .times. i /
i .times. S .alpha. .times. .times. i .times. I .alpha. .times.
.times. i .times. .times. q .beta. = i .times. S .beta. .times.
.times. i .times. R i .times. I .beta. .times. .times. i / i
.times. S .beta. .times. .times. i .times. I .beta. .times. .times.
i .times. .times. q .gamma. = i .times. S .gamma. .times. .times. i
.times. R i .times. I .gamma. .times. .times. i / i .times. S
.gamma. .times. .times. i .times. I .gamma. .times. .times. i
.times. .times. q .delta. = i .times. S .delta. .times. .times. i
.times. R i .times. I .delta. .times. .times. i / i .times. S
.delta. .times. .times. i .times. I .delta. .times. .times. i
Equation .times. .times. ( 3 ) ##EQU3##
[0061] By plugging the predicted reflectance R(.lamda.) of Eq. (1a)
or (1b) as discrete reflectance vector components R.sub.i into
equations (3), the sensor responses q.sub..alpha., q.sub..beta.,
q.sub..gamma., q.sub..delta., are predicted as a function of the
surface coverages a.sub.j of the colorants. One may also consider
converting the sensor responses q.sub.k into sensor densities
responses D.sub.k according to the equation
D.sub.k=-log.sub.10(q.sub.k) (2b)
[0062] The well known Demichel equations (4) yield the colorant
(also called Neugebauer primaries) surface coverages a.sub.i as a
function of the ink surface coverages c.sub.1, c.sub.2, c.sub.3,
and c.sub.4 of the inks i.sub.1, i.sub.2, i.sub.3, and i.sub.4.
i.sub.1 alone: a.sub.1=c.sub.1(1-c.sub.2)(1-c.sub.3)(1-c.sub.4)
i.sub.2 alone: a.sub.2=(1-c.sub.1)c.sub.2(1-c.sub.3)(1-c.sub.4)
i.sub.3 alone: a.sub.3=(1-c.sub.1)(1-c.sub.2)c.sub.3(1-c.sub.4)
i.sub.4 alone: a.sub.4=(1-c.sub.1)(1-c.sub.2)(1-c.sub.3)c.sub.4
i.sub.1 and i.sub.2: a.sub.5=c.sub.1 c.sub.2(1-c.sub.3)(1-c.sub.4)
i.sub.1 and i.sub.3: a.sub.6=c.sub.1(1-c.sub.2)c.sub.3(1-c.sub.4)
i.sub.i and i.sub.4: a.sub.7=c.sub.1(1-c.sub.2)(1-c.sub.3)c.sub.4
i.sub.2 and i.sub.3: a.sub.8=(1-c.sub.1)c.sub.2 c.sub.3(1-c.sub.4)
i.sub.2 and i.sub.4: a.sub.9=(1-c.sub.1)c.sub.2(1-c.sub.3)c.sub.4
i.sub.3 and i.sub.4: a.sub.10=(1-c.sub.1)(1-c.sub.2)c.sub.3 c.sub.4
i.sub.1, i.sub.2 and i.sub.3: a.sub.11=c.sub.1 c.sub.2
c.sub.3(1-c.sub.4) i.sub.2, i.sub.3 and i.sub.4:
a.sub.12=(1-c.sub.1)c.sub.2 c.sub.3 c.sub.4 i.sub.i, i.sub.3 and
i.sub.4: a.sub.13=c.sub.1(1-c.sub.2)c.sub.3 c.sub.4 i.sub.i,
i.sub.2 and i.sub.4: a.sub.14=c.sub.1 c.sub.2(1-c.sub.3)c.sub.4
i.sub.1, i.sub.2, i.sub.3 and i.sub.4: a.sub.15=c.sub.1 c.sub.2
c.sub.3 c.sub.4 substrate white:
a.sub.16=(1-c.sub.1)(1-c.sub.2)(1-c.sub.3)(1-c.sub.4). Equations
(4)
[0063] For more information on the computation of the colorant
surface coverages from ink surface coverages, see [Wyble and Berns
2000].
[0064] Let us consider the Clapper-Yule based spectral prediction
model. By inserting the relative amounts of colorants a.sub.i and
their transmittances t.sub.i into Equation (1), we obtain a
predicted reflection spectrum of a color patch printed with given
surface coverages of cyan, magenta, yellow and black. Both the
specular reflection r.sub.s and the internal reflection r.sub.i
depend on the refraction indices of the air (n.sub.1=1) and of the
paper (n.sub.2=1.5 for paper). According to the Fresnel equations
(see E. Hecht, Schaum's Outline of Optics, McGraw-Hill, 1974,
Chapter 3), for collimated light at an incident angle of
45.degree., the specular reflection factor is r.sub.s=0.05. With
light diffusely reflected by the paper (Lambert radiator), the
internal reflection factor is r.sub.i=0.6 (see D. B. Judd, Fresnel
reflection of diffusely incident light, Journal of Research of the
National Bureau of Standards, Vol. 29, November 42, 329-332). To
put the model into practice, we deduce from Equation (1a) the
internal reflectance spectrum r.sub.g of a blank paper by setting
all the ink surface coverages different from white as zero r g = R
g - K * r S 1 + ( 1 - K ) * r i * r S + r i * R g - r S .times. r i
( 5 ) ##EQU4## where R.sub.g is the measured unprinted paper
reflectance.
[0065] We then calculate the transmittance of each individual solid
colorant (solid inks and solid ink superposition) t.sub.c, t.sub.m,
t.sub.y, t.sub.k, t.sub.cm, t.sub.cy, t.sub.ck, t.sub.my, t.sub.mk,
t.sub.yk, t.sub.cmy, t.sub.myk, t.sub.cyk, t.sub.cmk, t.sub.cmyk,
t.sub.w by inserting into Eq. (1a) the measured solid colorant
reflectance R.sub.i and by setting the appropriate colorant surface
coverage a.sub.i=1 and all other colorant coverages
a.sub.j.noteq.i=0. The transmittance of solid colorant i becomes t
i = R i - K * r S r g * r i * ( R i - K * r S ) + r g * ( 1 - r i )
* ( 1 - r S ) ( 6 ) ##EQU5##
[0066] We should also take ink spreading into account, i.e. the
increase in effective (physical) dot surface coverage. We can fit
effective surface coverages either by minimizing a distance metric
either between measured and predicted reflection spectra or between
measured and predicted multi-channel sensor responses.
[0067] During an initial calibration, for each ink u, we fit
according to the selected spectral prediction model (CYSN, Eq. (1a)
or YNSN, Eq. (1b), with a.sub.j=u being fitted and
a.sub.j.noteq.u=0) the unknown effective surface coverages
f.sub.u(c.sub.u) of the measured single ink patches at nominal
coverages c.sub.u of e.g. 25%, 50%, 75%, 100% by minimizing a
distance metric between predicted and measured reflection spectra
or multi-channel sensor responses.
[0068] Similarly, we fit the unknown effective surface coverages
f.sub.u/v(c.sub.u) of single ink halftones of ink u printed in
superposition with a second solid ink v at nominal surface
coverages c.sub.u (e.g. at 25%, 50% and 75%) with the selected
spectral prediction model, Eq. (1a) or (1b), with halftone surface
coverage f.sub.u/v(c.sub.u)=a.sub.u being fitted, a second solid
ink a.sub.v=1 and all other colorant surface coverages
a.sub.(j.noteq.u,j.noteq.v)=0, by minimizing a distance metric
between predicted and measured reflection spectra, respectively
multi-channel sensor responses. The same procedure is applied for
fitting the unknown effective surface coverages f.sub.u/vw(c.sub.u)
of single ink halftones a.sub.u printed in superposition with two
solid inks, Eq. (1a) or (1b), with halftone surface coverage
f.sub.u/vw(c.sub.u)=a.sub.j=u being fitted, a second solid ink
a.sub.v=1, a 3.sup.rd solid ink a.sub.w=1 and 4.sup.th ink surface
coverage a.sub.(j.noteq.u,j.noteq.v,j.noteq.w)=0. The same
procedure is also applied for fitting the unknown effective surface
coverages f.sub.u/vwz(c.sub.u) of single ink halftones of ink u
printed in superposition with three solid inks, Eq. (1a) or (1b),
with halftone surface coverage f.sub.u/vwz(c.sub.u)=a.sub.j=u being
fitted, a second solid ink a.sub.v=1, a 3.sup.rd solid ink
a.sub.w=1 and a 4.sup.th solid ink a.sub.z=1. Each set of fitted
effective surface coverages (e.g. at nominal surface coverages
c.sub.u=25%, 50% and 75%) maps nominal surface coverages to
effective surface coverages for that superposition condition. For a
given superposition condition, by interpolating between the known
mappings between nominal to effective surface coverages, we obtain
a function mapping between nominal to effective surface coverages.
This function is called "effective surface coverage curve" or
"effective coverage curve".
[0069] In order to obtain the effective surface coverages c.sub.1,
c.sub.2, c.sub.3 and c.sub.4 of a color halftone patch from their
nominal coverages c.sub.1n, c.sub.2n c.sub.3n, and c.sub.4n and
then, with the Demichel equations (4), to obtain the corresponding
effective colorant surface coverages a.sub.j to be inserted in the
spectral prediction model equation (1a), or respectively (1b), it
is necessary to weight the contributions of the corresponding
effective coverage curves. The weighting functions depend on the
effective coverages of the considered ink alone, of the considered
ink in superposition with a second ink, of the considered ink in
superposition with the two other inks and of the considered ink in
superposition with the three other inks. For the considered system
of 4 inks i.sub.1, i.sub.2, i.sub.3 and i.sub.4 with nominal
coverages c.sub.1n c.sub.2n, c.sub.3n and c.sub.4n and effective
coverages c.sub.1, c.sub.2, c.sub.3 and c.sub.4, assuming that inks
are printed independently of each other, e.g. according to the
classical screen angles 15.degree., 45.degree., 75.degree. and
0.degree., by computing the relative weight, i.e. the relative
surface of each superposition condition, we obtain the system of
equations (7) published in [Hersch05A]. c 1 = f 1 .times. ( c 1
.times. .times. n ) .times. ( 1 - c 2 ) .times. ( 1 - c 3 ) .times.
( 1 - c 4 ) + f .times. 1 | 2 .times. ( c 1 .times. .times. n )
.times. c 2 .times. ( 1 - c 3 ) .times. ( 1 - c 4 ) + f .times. 1 |
3 .times. ( c 1 .times. .times. n ) .times. ( 1 - c 2 ) .times. c 3
.times. ( 1 - c 4 ) + f .times. 1 | 4 .times. ( c 1 .times. .times.
n ) .times. ( 1 - c 2 ) .times. ( 1 - c 3 ) .times. c 4 + f .times.
1 | 23 .times. ( c 1 .times. .times. n ) .times. c 2 .times. c 3
.times. ( 1 - c 4 ) + f .times. 1 | 24 .times. ( c 1 .times.
.times. n ) .times. c 2 .times. ( 1 - c 3 ) .times. c 4 + f .times.
1 | 34 .times. ( c 1 .times. .times. n ) .times. ( 1 - c 2 )
.times. c 3 .times. c 4 + f .times. 1 | 234 .times. ( c 1 .times.
.times. n ) .times. c 2 .times. c 3 .times. c 4 .times. .times. c 1
= f 2 .times. ( c 2 .times. .times. n ) .times. ( 1 - c 1 ) .times.
( 1 - c 3 ) .times. ( 1 - c 4 ) + f .times. 2 | 1 .times. ( c 2
.times. .times. n ) .times. c 1 .times. ( 1 - c 3 ) .times. ( 1 - c
4 ) + f .times. 2 | 3 .times. ( c 2 .times. .times. n ) .times. ( 1
- c 1 ) .times. c 3 .times. ( 1 - c 4 ) + f .times. 2 | 4 .times. (
c 2 .times. .times. n ) .times. ( 1 - c 1 ) .times. ( 1 - c 3 )
.times. c 4 + f .times. 2 | 13 .times. ( c 2 .times. .times. n )
.times. c 1 .times. c 3 .times. ( 1 - c 4 ) + f .times. 2 | 24
.times. ( c 2 .times. .times. n ) .times. c 1 .times. ( 1 - c 3 )
.times. c 4 + f .times. 2 | 34 .function. ( c 2 .times. .times. n )
.times. ( 1 - c 1 ) .times. c 3 .times. c 4 + f .times. 2 | 134
.function. ( c 2 .times. .times. n ) .times. c 1 .times. c 3
.times. c 4 .times. .times. c 3 = f 3 .times. ( c 3 .times. .times.
n ) .times. ( 1 - c 1 ) .times. ( 1 - c 2 ) .times. ( 1 - c 4 ) + f
.times. 3 | 1 .times. ( c 3 .times. .times. n ) .times. c 1 .times.
( 1 - c 2 ) .times. ( 1 - c 4 ) + f .times. 3 | 2 .times. ( c 3
.times. .times. n ) .times. ( 1 - c ) .times. c 2 .times. ( 1 - c 4
) + f .times. 3 | 4 .times. ( c 3 .times. .times. n ) .times. ( 1 -
c 1 ) .times. ( 1 - c 2 ) .times. c 4 + f 3 | 12 .times. ( c 3
.times. .times. n ) .times. c 1 .times. c 2 .times. ( 1 - c 4 ) + f
3 | 14 .times. ( c 3 .times. .times. n ) .times. c 1 .times. ( 1 -
c 2 ) .times. c 4 + f 3 | 24 .times. ( c 3 .times. .times. n )
.times. ( 1 - c 1 ) .times. c 2 .times. c 4 + f .times. 3 | 124
.function. ( c 3 .times. .times. n ) .times. c 1 .times. c 2
.times. c 4 .times. .times. c 4 = f 4 .times. ( c 4 .times. .times.
n ) .times. ( 1 - c 1 ) .times. ( 1 - c 2 ) .times. ( 1 - c 3 ) + f
4 | 1 .times. ( c 4 .times. .times. n ) .times. c 1 .times. ( 1 - c
2 ) .times. ( 1 - c 3 ) + f 4 | 2 .times. ( c 4 .times. .times. n )
.times. ( 1 - c 1 ) .times. c 2 .times. ( 1 - c 3 ) + f 4 | 3
.times. ( c 4 .times. .times. n ) .times. ( 1 - c 1 ) .times. ( 1 -
c 2 ) .times. c 3 + f 4 | 12 .times. ( c 4 .times. .times. n )
.times. c 1 .times. c 2 .times. ( 1 - c 3 ) + f 4 | 13 .times. ( c
4 .times. .times. n ) .times. c 1 .times. ( 1 - c 2 ) .times. c 3 +
f 4 | 23 .times. ( c 4 .times. .times. n ) .times. ( 1 - c 1 )
.times. c 2 .times. c 3 + f 4 | 123 .function. ( c 4 .times.
.times. n ) .times. c 1 .times. c 2 .times. c 3 . Equation .times.
.times. ( 7 ) ##EQU6##
[0070] This system of equations requires the acquisition of 32
effective coverage curves (all f functions). In the case of cyan,
magenta, yellow and black inks, this system of equations may be
simplified by assuming that any halftone printed on the solid black
ink results anyway in a color very close to black and does not
modify the ratio between the weights of the effective surface
coverage curves of that halftone. For black ink halftones however,
the surface coverage curves depend on the different superposition
conditions and are therefore kept intact. We therefore obtain for
cyan, magenta, yellow and black inks the simplified set of
effective surface coverage equations (8), mapping nominal surface
coverages c.sub.n m.sub.n, y.sub.n and k.sub.n to effective surface
coverages c, m, y and k. c = f c .times. ( c n ) .times. ( 1 - m )
.times. ( 1 - y ) + f .times. c | m .times. ( c n ) .times. m
.times. ( 1 - y ) + f c | y .times. ( c n ) .times. ( 1 - m )
.times. y + f c | my .function. ( c n ) .times. m .times. .times. y
.times. .times. m = f m .function. ( m n ) .times. ( 1 - c )
.times. ( 1 - y ) + f .times. m | c .function. ( m n ) .times. c
.function. ( 1 - y ) + f .times. m | y .function. ( m n ) .times. (
1 - c ) .times. y + f .times. f | cy .function. ( m n ) .times. c
.times. .times. y .times. .times. y = f y .function. ( y n )
.times. ( 1 - c ) .times. ( 1 - m ) + f .times. y | c .function. (
y n ) .times. c .function. ( 1 - m ) + f .times. y | m .function. (
y n ) .times. ( 1 - c ) .times. m + f .times. y | cm .function. ( y
n ) .times. c .times. .times. m .times. .times. k = f k .function.
( k n ) .times. ( 1 - c ) .times. ( 1 - m ) .times. ( 1 - y ) + f k
| c .function. ( k n ) .times. c .function. ( 1 - m ) .times. ( 1 -
y ) + f k | m .function. ( k n ) .times. ( 1 - c ) .times. m
.function. ( 1 - y ) ++ .times. f k | y .function. ( k n ) .times.
( 1 - c ) .times. ( 1 - m ) .times. y + f k | cm .function. ( k n )
.times. c .times. .times. m .function. ( 1 - y ) + f k | cy
.function. ( k n ) .times. c .function. ( 1 - m ) .times. y + f k |
my .function. ( k n ) .times. ( 1 - c ) .times. m .times. .times. y
+ f k | cmy .function. ( k n ) .times. c .times. .times. m .times.
.times. y . Equation .times. .times. ( 8 ) ##EQU7##
[0071] Here, only 20 effective surface coverage curves need to be
acquired. This system of equations can be solved by first assigning
the nominal surface coverages c.sub.n m.sub.n, y.sub.n and k.sub.n
to the corresponding effective surface coverages c, m, y and k and
then by performing several iterations, typically 5 iterations,
until the system converges.
[0072] This reduction in the number of surface coverage curves and
simplification of the ink spreading equations will be published on
the 29.sup.th of Jan. 2008 in a paper by T. Bugnon, R. D. Hersch,
Simplified ink spreading equations for CMYK prints, in SPIE Vol.
6807.
Scalar Initial Thicknesses
[0073] The accurate computation of ink thickness variations
requires an explicit expression of ink transmittances.
Transmittances may be deduced from measured reflectances with any
spectral prediction model, in which the ink transmittances are
explicitly expressed.
[0074] In most printing processes, there is trapping, i.e. the
respective ink thicknesses of superposed inks are modified
(generally reduced). The disclosed ink thickness variation
computation model takes care of trapping by computing the internal
transmittances t.sub.ij of colorants obtained by the superposition
of two inks, of three inks t.sub.ijk and of four inks t.sub.ijkl
from the internal transmittance of the individual inks t.sub.c,
t.sub.m, t.sub.y, t.sub.k and from their respective fitted reduced
thicknesses. For each superposition of solid inks we compute their
respective thicknesses, called "initial thicknesses".
[0075] For each solid ink contributing to a superposition of solid
inks, called "solid colorant", each solid ink wavelength-dependent
spectral transmittance has an initial scalar thickness. Since we
perform computations with relative thickness values, the initial
thickness of a single ink is one. For two superposed inks i and j,
two initial thicknesses d.sub.Ij and d.sub.iJ for the inks i and j
respectively are fitted, by starting from a unit thickness. The
same applies for 3 inks or for 4 inks. In Eqs. (9) below, for
example, the initial thickness d.sub.iJk expresses the initial
thickness of ink j, when superposed with inks i and k. The initial
thickness d.sub.ijK expresses the initial thickness of ink k, when
superposed with inks i and j. Similar denominations apply for the
other initial thicknesses. t(.lamda.).sub.ij={circumflex over
(t)}.sub.i(.lamda.).sup.d.sup.Ij*{circumflex over
(t)}.sub.j(.lamda.).sup.d.sub.iJ t(.lamda.).sub.ijk={circumflex
over (t)}.sub.i(.lamda.).sup.d.sup.Ijk*{circumflex over
(t)}.sub.j(.lamda.).sup.d.sup.iJk*{circumflex over
(t)}.sub.k(.lamda.).sup.d.sup.ijK t(.lamda.).sub.ijkl={circumflex
over (t)}.sub.i(.lamda.).sup.d.sup.Ijkl*{circumflex over
(t)}.sub.j(.lamda.).sup.d.sup.iJkl*{circumflex over
(t)}.sub.k(.lamda.).sup.d.sup.ijKl*{circumflex over
(t)}.sub.l(.lamda.).sup.d.sup.ijkL Equations (9) where {circumflex
over (t)}.sub.i(.lamda.),{circumflex over
(t)}.sub.j(.lamda.),{circumflex over
(t)}.sub.k(.lamda.),{circumflex over (t)}.sub.l(.lamda.) are
respectively the initially computed wavelength-dependent
transmittances of single solid inks i, j, k, l of the calibration
patches, calculated according to Eq. (6). By inserting the colorant
transmittances t(.lamda.).sub.ij, t(.lamda.).sub.ijk,
t(.lamda.).sub.iklj of Eqs. (8) for all ink superposition
conditions into Eq. (1a), or respectively (1b), the underlying
spectral prediction model becomes an ink thickness enhanced
spectral prediction model.
Ink Thickness Variation Factors
[0076] The introduction of ink thickness variation factors within
the spectral prediction model allows the deduction of ink thickness
variations from spectral light reflectance, respectively the
multi-channel light reflectance sensor response of halftones. Such
halftones are generally present at specific locations within a
printed page (e.g. within a reproduced color image). We introduce
the ink thickness variations into Eqs. (9) by multiplying each
initial ink thickness with a scalar ink thickness variation factor
(also simply called "ink thickness variation"). There is one ink
thickness variation factor per contributing ink and it does not
depend on the superposition condition, i.e. with which other ink
(or inks) the considered ink is superposed. The transmittances of
single ink, two ink, three ink and four ink solid colorants are
expressed by ink transmittances (symbol: {circumflex over (t)})
initial ink thicknesses (symbol: d) and ink thickness variation
factors (symbol: dr), see Eqs. (10). t(.lamda.).sub.i={circumflex
over (t)}.sub.i(.lamda.).sup.dr.sup.i t(.lamda.).sub.ij={circumflex
over (t)}.sub.i(.lamda.).sup.d.sup.Ij*.sup.dr.sup.i*{circumflex
over (t)}.sub.j(.lamda.).sup.d.sup.iJ*.sup.dr.sup.j
t(.lamda.).sub.ijk={circumflex over
(t)}.sub.i(.lamda.).sup.d.sup.ijk*.sup.dr.sup.i*{circumflex over
(t)}.sub.j(.lamda.).sup.d.sup.iJk*.sup.dr.sup.j*{circumflex over
(t)}.sub.k(.lamda.).sup.d.sup.ijK*.sup.dr.sup.k
t(.lamda.).sub.ijkl={circumflex over
(t)}.sub.i(.lamda.).sup.d.sup.Ijkl*.sup.dr.sup.i*{circumflex over
(t)}.sub.j(.lamda.).sup.d.sup.iJkl*.sup.dr.sup.j*{circumflex over
(t)}.sub.k(.lamda.).sup.d.sup.ijKl*.sup.dr.sup.k*{circumflex over
(t)}.sub.l(.lamda.).sup.d.sup.ijkL*.sup.dr.sup.l Equations (10)
where the thickness variation factor of ink i is dr.sub.i, of ink j
is dr.sub.j, of ink k is dr.sub.k and of ink l is dr.sub.l.
[0077] In the case of cyan, magenta, yellow and black inks, we
express the 16 colorant transmittances as follows.
t.sub.C={circumflex over (t)}.sub.C.sup.dr.sup.C; transmittance of
solid colorant cyan t.sub.M={circumflex over
(t)}.sub.M.sup.dr.sup.M; transmittance of solid colorant magenta
t.sub.Y={circumflex over (t)}.sub.Y.sup.dr.sup.Y; transmittance of
solid colorant yellow t.sub.K={circumflex over
(t)}.sub.T.sup.dr.sup.K; transmittance of solid colorant black
t.sub.CM={circumflex over
(t)}.sub.C.sup.d.sup.Cm*.sup.dr.sup.C*{circumflex over
(t)}.sub.M.sup.d.sup.cM*.sup.dr.sup.M; transmittance of solid
colorant cyan+magenta (blue) t.sub.CY={circumflex over
(t)}.sub.C.sup.d.sup.Cm*.sup.dr.sup.C*{circumflex over
(t)}.sub.Y.sup.d.sup.cY*.sup.dr.sup.Y; transmittance of solid
colorant cyan+yellow (green) t.sub.CK={circumflex over
(t)}.sub.C.sup.d.sup.Cm*.sup.dr.sup.C*{circumflex over
(t)}.sub.K.sup.d.sup.cK*.sup.dr.sup.K; transmittance of solid
colorant cyan+black t.sub.MY={circumflex over
(t)}.sub.M.sup.d.sup.My*.sup.dr.sup.M*{circumflex over
(t)}.sub.Y.sup.d.sup.mY*.sup.dr.sup.Y; transmittance of solid
colorant magenta+yellow (red) t.sub.MK={circumflex over
(t)}.sub.M.sup.d.sup.Mk*.sup.dr.sup.M*{circumflex over
(t)}.sub.K.sup.d.sup.mK*.sup.dr.sup.K; transmittance of solid
colorant magenta+black t.sub.YK={circumflex over
(t)}.sub.Y.sup.d.sup.Mk*.sup.dr.sup.Y*{circumflex over
(t)}.sub.K.sup.d.sup.yK*.sup.dr.sup.K; transmittance of solid
colorant yellow+black t.sub.CMY={circumflex over
(t)}.sub.C.sup.d.sup.Cmy*.sup.dr.sup.C*{circumflex over
(t)}.sub.M.sup.d.sup.cMy*.sup.dr.sup.M*{circumflex over
(t)}.sub.Y.sup.d.sup.cmY*.sup.dr.sup.Y; transmittance of
cyan+magenta+yellow t.sub.MYK={circumflex over
(t)}.sub.M.sup.d.sup.Myk*.sup.dr.sup.M*{circumflex over
(t)}.sub.Y.sup.d.sup.mYk*.sup.dr.sup.Y*{circumflex over
(t)}.sub.K.sup.d.sup.myK*.sup.dr.sup.K; transmittance of
magenta+yellow+black t.sub.CYK={circumflex over
(t)}.sub.C.sup.d.sup.Cyk*.sup.dr.sup.C*{circumflex over
(t)}.sub.Y.sup.d.sup.cYk*.sup.dr.sup.Y*{circumflex over
(t)}.sub.K.sup.d.sup.cyK*.sup.dr.sup.K; transmittance of
cyan+yellow+black t.sub.CMK={circumflex over
(t)}.sub.C.sup.d.sup.Cmk*.sup.dr.sup.C*{circumflex over
(t)}.sub.M.sup.d.sup.cMk*.sup.dr.sup.M*{circumflex over
(t)}.sub.K.sup.d.sup.cmK*.sup.dr.sup.K; transmittance of
cyan+magenta+black t.sub.CMYK={circumflex over
(t)}.sub.C.sup.d.sup.Cmyk*.sup.dr.sup.C*{circumflex over
(t)}.sub.M.sup.d.sup.cMyk*.sup.dr.sup.M*{circumflex over
(t)}.sub.Y.sup.d.sup.cmYk*.sup.dr.sup.Y*{circumflex over
(t)}.sub.K.sup.d.sup.cmyK*.sup.dr.sup.K; transmittance of
cyan+magenta+yellow+black t.sub.W={circumflex over (t)}.sub.W;
transmittance of unprinted paper, by definition equal to 1 at all
wavelengths. Equations (11)
[0078] In the solid colorant transmittances above (Eqs. 11), the
superposition dependent initial thicknesses are calibrated during
the calibration phase according to equations (9). At printing time,
the ink thickness variation factors are the fitted unknowns. For
cmyk inks, the thickness variation factors of cyan, magenta, yellow
and black are respectively dr.sub.C, dr.sub.M, dr.sub.Y and
dr.sub.K. The ink thickness variation computation model now
consists of Eqs. (1a) or (1b, 1c), and (3) in which transmittances
t.sub.1 to t.sub.16 are expressed by the 16 transmittances present
in Eqs. (11), which in the case of 4 inks are a function of the 4
ink thickness variation factors.
[0079] The spectral ink thickness variation computation model
enables obtaining the ink thickness variations of a printing system
(a) on specially defined test patches, (b) on freely chosen print
image locations and (c) with sensor responses over an area segment
(i.e. along a thin rectangle of a short length, e.g. 1 mm.times.2
mm or 2 mm.times.2 mm) within the printed sheet. Only nominal
surface coverages, as defined by the prepress system, need to be
known. Accurate ink thickness variation factors can be fitted
thanks to the ink thickness variation computation model once the
initial ink thicknesses are fitted and the effective coverage
curves have been established (either during offline initial
calibration or during online calibration). With the effective
surface coverage curves, nominal surface coverages of inks are
mapped into effective surface coverages of inks, from which the
effective surface coverages a.sub.j of the colorants are computed
according to the Demichel equations (4) and inserted into
respectively Eq. (1a) or Eq. (1b).
[0080] As an example, FIG. 1 shows a diagram of the ink thickness
variation computation model for the three inks cyan, magenta and
yellow. It comprises respectively the input nominal surface
coverages of c, m, and y 101, the operation 102 of weighting the
surface coverages curves 112 according to the surface coverages of
the underlying colorants in order to obtain the effective surface
coverages c', m', and y' 105, the computation 103 of the effective
colorant coverages according to the Demichel equations, the ink
thickness and sensor response extended spectral prediction model
104, which receives as input 109 measured sensor responses (e.g.
q.sub.r, q.sub.g, q.sub.b) or sensor density responses. Initially
calibrated parameters comprise the ink transmittances 106, and the
initial ink thicknesses 107 for each ink in each possible ink
superposition (colorant). Parameters calibrated or recalibrated at
the beginning of the print session comprise the paper or substrate
reflectance r.sub.g 108 and possibly the effective coverage curves
for each useful superposition condition 113. The output of the
model are the cyan, magenta, and yellow ink thickness variation
factors 110 dr.sub.C, dr.sub.M, and dr.sub.Y. In FIG. 1, the
initial thicknesses 107 are labeled d.sub.i for the initial
thickness of a single ink, d.sub.Ij for the initial thickness of
one ink i superposed with another ink j, and d.sub.Ijk for the
initial thickness of one ink i superposed with two other inks j and
k. Ink layers I, j, and k are different one from another and are
placeholders for every of the 3 possible inks of the considered
printing system.
[0081] FIG. 2 is a generalization of the ink thickness variation
computation model shown in FIG. 1, for a freely chosen set of four
different inks I.sub.1, I.sub.2, I.sub.3, I.sub.4 of surface
coverages c.sub.1, c.sub.2, c.sub.3, c.sub.4 (201). Box 211
represents the connections between input nominal ink surface
coverages 201 and their corresponding mappings 212 to effective
surface coverages f.sub.i(i), f.sub.i/j(i), f.sub.i/jk(i),
f.sub.i/jkl(i) in the different superposition conditions i alone, i
superposed with j, i superposed with j and k, and i superposed with
j, k and l, where i,j, k and l are placeholders for any of the inks
and are different one from another. Surface coverages 213 are
weighted 202 according to the underlying colorants to yield after a
few iterations the effective surface coverages c.sub.1', c.sub.2',
c.sub.3', c.sub.4' (205). Thanks to the colorant computation
equations 203 (Demichel in case of independently printed ink
layers, Eqs, (4)), one obtains the effective surface coverages
a.sub.1', to a.sub.16' (214). The ink thickness variation extended
spectral prediction model 204 fits the ink thickness variations 210
dr.sub.1 of ink I.sub.1, dr.sub.2 of ink I.sub.2, dr.sub.3 of ink
I.sub.3 and dr.sub.4 of ink I.sub.4 by minimizing a difference
metric between predicted sensor responses and measured sensor
responses 209 q.sub..alpha., q.sub..beta., q.sub..gamma.,
q.sub..delta. or possibly by minimizing the sum of such differences
for several measurements performed at different locations of the
printed sheet. Parameters deduced either from initial offline or
from online calibration comprise the ink transmittances 206, the
initial ink thicknesses 207 for each ink superposed with any other
combination of inks (different colorants) and the paper reflectance
208 (R.sub.g). One possible embodiment of the ink thickness
variation computation model of FIG. 2 is a printing system printing
with cyan, magenta, yellow and black inks.
[0082] The ink thickness variation computation model shown in FIG.
2 can be extended in several ways.
[0083] (A): Instead of having 4 sensors with spectral sensibilities
in different parts of the considered wavelength range (visible and
near infra-red), one may introduce 5, 6 or more sensors.
[0084] (B): Since some printing systems employ more than 4 inks
(examples of possible additional inks: orange, dark blue, light
cyan, light magenta, etc.), the ink thickness variation computation
model can be correspondingly extended by introducing as many
nominal 201 and effective 205 ink surface coverages as inks, as
many transmittances 206 as inks, as many effective surface coverage
curves 212 and initial ink thicknesses 207 as used superpositions
of inks and as many ink thickness variations 214 as inks.
Calibration of the Ink Thickness Variation Computation Model
[0085] We distinguish between an off-line initial calibration for a
given class of printers, papers and inks and an optional online
calibration (or recalibration) for further tuning the calibration
according to the current printing conditions (current paper,
currently used set of inks, current temperature, etc.). The
off-line calibration may be performed once (e.g. in the factory) by
the manufacturer of the printer, respectively printing press, and
delivered or made available for download as a computer file, called
"initial calibration data".
[0086] The initial calibration of the ink thickness variation
computation model comprises the deduction of the transmittances of
the inks and possibly an initial set of effective surface coverage
curves. During the initial calibration, the spectral reflectances
of unprinted paper and of all solid ink and solid ink
superpositions are measured by a spectral measurement device. The
Clapper-Yule internal reflectance of paper r.sub.g(.lamda.) is
deduced by equation (5). Ink and colorant transmittances are
deduced according to Eq. (6) or respectively according to Eq. (1c).
For 4 inks (e.g. cmyk), 16 spectral measurements are needed.
Initial thicknesses are fitted according to equations (9), by
minimizing a distance metric between predicted colorant
transmittance spectra and transmittance spectra deduced from
colorant measurements.
[0087] Initial surface coverage curves can be obtained for the
relevant superposition conditions by measuring single halftone
patches (e.g. at 50% nominal surface coverages), deducing the
corresponding effective surface coverages and creating the
effective surface coverages curves by interpolation. According to
Equations (8), for creating 20 surface coverage curves, one needs
60 measurements in the case of 3 different surface coverages per
curve or 20 measurements in the case of a single surface coverage
measurement per curve. For deducing effective surface coverages
during the initial calibration, one may measure halftone
reflectance spectra and fit the effective surface coverages by
minimizing a distance metric between reflection spectra predicted
according to Equation (1a) or (1b) and measured reflection spectra.
Alternately, for example during online calibration, one may measure
the sensor responses q.sub..alpha., q.sub..beta., q.sub..gamma.,
q.sub.67 and, according to Equations (1a) or (1b) and (3), fit the
surface coverages by minimizing a distance metric between predicted
and measured sensor responses.
[0088] The online calibration (when applicable also called
recalibration) of the paper reflectance (from which in the case of
the CYSN model the internal paper reflectance is derived) and of
the surface coverage curves is performed on the running printer or
press, once the prints match the desired quality. The recalibration
of the paper reflectance R.sub.g(.lamda.) according to the current
print sheet paper is performed by replacing the paper reflectance
R.sub.g'(.lamda.) measured during the initial calibration by a
scaled paper reflectance
R.sub.g(.lamda.)=R.sub.g'(.lamda.)s.sub.g+o.sub.g, where s.sub.g is
a scaling factor and o.sub.g is an offset, both fitted by
minimizing a difference metric between sensor responses predicted
according to Eq. (3) for white paper and corresponding measured
sensor responses. To reduce the impact of noise, it is preferable
to minimize the sum of such difference metrics for several paper
locations.
[0089] The online calibration (or recalibration) of the effective
surface coverage curves is performed as follows. Sensor response
predictions for a number of area segments are made on the running
press, by considering the effective surface coverage curves as free
variables and by fitting these effective surface coverage curves so
as to minimize the sum of a difference metric between measured
sensor responses and predicted sensor responses at different
printed sheet locations. A surface coverage curve may be given as a
corresponding dot gain curve, where dot gain is defined as the
effective surface coverage minus the nominal surface coverage. The
dot gain curve may be given by a single quadratic Bezier spline
through the points (0,0), (0.5, dot gain at 50%), (1,0). In a
preferred implementation, this dot gain curve, and therefore the
corresponding surface coverage curve are fitted by fitting the dot
gain at 50% surface coverage.
Layout of Illuminating and Sensing Devices
[0090] Since there may be variations of ink thicknesses across the
printed sheet, compact elements comprising the illuminating and
sensing devices (FIG. 3, 308) may be repeated along the width 321
of the printed sheet 301, or in the case of a printing press, at
the location of each ink zone. These elements are positioned on the
path way of the printed paper. In a preferred embodiment (FIG. 5A),
each sensor of a 4-channel sensor device may have its own
illumination, either white light including infra-red components or
light formed by a light emitting diode (LED) emitting within a
given spectral wavelength range. In a possible embodiment, the
light 505 is guided by a waveguide 503 (or by an optical fiber) to
hit the print surface 501 at an angle of approximately 45 degrees.
In the case of white light, a corresponding red, green, blue or
infra-red filter 509 is placed in the light path of the light, e.g.
at the entry of the waveguide. Optionally, the waveguide for the
incident light may be terminated by a lens (possibly Fresnel
lenses) 506 which focus the light onto the print surface. In
alternative embodiments, the filters may be integrated into the
lens by diffusing a partially absorbing layer at its surface or by
creating the lens with a partially absorbing substance. In a
further alternative, the waveguide may also be conceived to filter
light at a given wavelength range.
[0091] To avoid capturing specular reflections, the reflected light
is captured perpendicularly to the print surface, e.g. by being
focused by a lens 507 in front of an optional waveguide 508. The
sensor 504 may either be directly in front of the print, or hooked
onto the waveguide 508 transmitting the light which is
perpendicularly reflected from the print surface. The sensor 504 is
generally an integrated circuit bonded onto an electronic circuit
board 502.
[0092] A compact embodiment (FIG. 5B) may comprise a light source
embodied by a LED 513 emitting in the blue, green, red or
respectively in the near-infra-red wavelength range, a polarizing
filter 514 polarizing the incident light according to a given
orientation, a waveguide 515 guiding the light to a beam splitter
510 which directs the incoming polarized light perpendicularly into
the print surface 517. The part of incident polarized light
specularly reflected at the surface of the print is discarded by a
second polarization filter 511 located on the reflected light path.
This output polarization filter is rotated by 90 degrees in respect
to the input polarization filter 514, located within the incident
light path. Light crossing the print interface 517 is transmitted
onto the print substrate (e.g. paper), is scattered within the
substrate and becomes depolarized. It is reflected by the
substrate, reaches the light guide, possibly through a focusing
lens, traverses the beam splitter in the reverse direction,
traverses the polarization filter 511 and strikes the sensor 512,
in the present embodiment, the SPAD (see next section). The two
polarizing filters discard specular reflections, which may be high
when the ink is still wet on the printed sheet. Many different
variants equivalent to the present embodiment may be considered,
such as using white light filtered by the input polarization filter
and further filtered by blue, green, red or infra-red filters
instead of a LED followed by the input polarization filter. As
previously, the sensor 512 is an integrated circuit located on a
printed circuit board 518 and the LED 513 is also connected 519 to
the printed circuit board.
[0093] In a further embodiment (FIG. 5C), the white light source
534 may have a certain length and illuminates the print sheet
parallel to the paper (537) moving orientation, along the
multi-channel sensing devices (530, 531, 532, 533). Alternately,
the elongated white light source may be positioned over the full
length of the printed sheet width and create the illumination for
many or all sensing devices. In both cases, each sensing device may
comprise a coating 530 with a filtering substance on the focalizing
lens 531. An optional waveguide 532 connects the lens to the sensor
533. These elements can be fixed on a printed circuit board
538.
[0094] Each of the 4 illuminating/sensor devices (FIGS. 3 and 4A,
304, 305, 306, 307) may be placed at successive positions within
the path of the moving paper. For example, by placing them one or
several millimeters apart one from another, reflected light
captured by one sensor device does not contribute to the light
captured by its neighbor device.
[0095] In order to capture ink thickness variations across the full
sheet width, 4-channel illuminating/sensor sets 308 may be placed
at regular intervals across the paper width, perpendicularly to the
paper displacement orientation. In the case of offset presses, it
is advisable to place at least one set of 4-channel
illuminating/sensor devices within each ink zone.
Processing of the Sensor Outputs
[0096] In the considered embodiment, the electronic signal (FIG.
4A, 401, 402, 403, 404) emitted by each of the 4 sensors according
to the illumination and the reflectance of the underlying sensed
area of the printed sheet depends on the sensor technology. The
preferred technology is the Single Photon Avalanche Diode (SPAD),
see S. Cova, et. al., Avalanche photodiodes and quenching circuits
for single-photon detection, Applied Optics, Vol. 35, Issue 12, pp.
1956-1976 (April 1996) as well as E. Charbon, Techniques for CMOS
Single Photon Imaging and Processing, 6.sup.th Intl. Conf. on ASIC,
IEEE ASICON 2005, pp. 1163-1168, referenced as [Charbon05], hereby
incorporated by reference. Other technologies, such as Charged
Coupled Devices (CCDs), or CMOS may also be used as sensors. The
advantage of SPADs is their ability of counting single photon
events, i.e. the possibility of very short sensor response
acquisition times, between hundreds of nanoseconds to a few
milliseconds and to provide a high signal to noise ratio, and
therefore a high effective dynamic range.
[0097] A set of multi-channel sensors may also be integrated within
a single integrated circuit (FIG. 4B, 440). In the embodiment of
FIG. 4B, a quadruplet of SPAD sensors 414, 415, 416, 417 comprise
their own pulse counters 424, 425, 426, 427 and counter output
lines 434, 435, 436, 437. The counter output lines may be
multiplexed by an internal multiplexer 438, whose output is
connected for example via a communication bus 460 to the sensing
system microcontroller 470. Such integrated multi-channel sensors
may be replicated over the width of the print sheet. An example is
the replicated multi-sensor integrated circuit 450, also connected
to the sensing system microcontroller 470 through the communication
bus 460.
[0098] In the case of paper printed at a speed of for example 2
meter per second (m/s), a sensor response acquisition time (also
called sensor active time or sensor aperture time) of one.
millisecond, counting the photons captured by the SPAD during 1 ms,
corresponds to a displacement of the paper by 2 mm, i.e. a sensed
area segment length of 2 mm. Within a printed sheet, many area
segments of this size have a nearly uniform color, i.e. are
composed of color pixels varying one in respect to an other by less
than CIELAB .DELTA.E.sub.94=6.
[0099] At a speed of 10 m/s, the sensed area segment length is 10
mm. Within a printed sheet either a close to uniform color area
segment of that size can be located within the printed sheet and in
the region of interest (e.g. an ink zone), or a non uniform area
segment is selected, and subdivided into parts. For predicting its
multi-channel sensor response, the multi-channel response of each
part is separately predicted. The predicted multi-channel sensor
response for the whole area segment is the weighted average of the
separately predicted parts, the weights corresponding to the
respective relative surfaces of these parts.
[0100] In the preferred SPAD embodiment, the sensing devices are
connected to a sensor processing module (FIGS. 3A and 3B, 320)
which comprises a multiplexer 310, a counter 311, fast logic (FL)
312 and a microcontroller (.mu.C) 316. The signal lines 309
arriving from the individual SPAD sensors transmit serially the
pulses corresponding to photon counts to a multiplexer (FIG. 3B,
310), which selects the sensor which must be currently read out.
The output of the multiplexer 314 is forwarded to the counter (CTR)
311 which is enabled 316 by the fast logic 312 to count the pulses
during the desired active time of the SPAD, i.e. during the passage
of a desired paper area segment beneath the corresponding sensor.
The output of the counter 315 giving the sensor response in term of
intensity is stored in the sensing system microcontroller 313
responsible for the control of the present sensing system. The
sensor devices 308 as well as the elements of the sensor processing
module 320 may be attached to a same printed circuit board 303.
[0101] The sensing processing module microcontroller 313 connected
to the main computing system (FIG. 7, 701) by a communication link
317 (FIGS. 3A and 3B), e.g. USB (Universal Serial Bus), receives
from the main computing system timing information about the
location of the area segments whose sensor responses need to be
read out. The sensing processing module microcomputer communicates
324 with the fast logic 312, for example embodied by a field
programmable gate array (FPGA), and initializes it to create the
signals 318 (FIGS. 3A, 3B and 4A) driving the sensor enabling
inputs, the signals driving the multiplexor 319, and the signals
resetting 325 and enabling 316 the counter 311. The sensing
processing module microcontroller 313 transmits the sensor
responses to the main computer. Thanks to the sensor responses, the
main computing system (FIG. 7, 701) computes the ink thickness
variations, and if necessary, applies corresponding corrections to
the printer actuation variables such as the amount of deposited
ink. For a printing press, deduction of ink thickness variations
enables automatically regulating the ink flow by acting on the
print actuation parameters such as the feed of ink.
[0102] In the case of sensors embodied by SPADs, one may conceive
an illuminating/sensing device where (a) all sensors continuously
provide pulses according to the incoming photons, (b) the
multiplexor 310 selects the currently active sensor and the fast
logic 312 defines the acquisition time and period by activating the
reset signal 325 of the counter before the acquisition and by
activating the counter's count enable signal 316 during the
acquisition period (few hundreds of nanoseconds to a few
milliseconds).
[0103] According to [Charbon05], the duration of a full photon
detection cycle called dead time (tD) is between 20 ns and 50 ns,
depending on the implementation of the SPAD. The maximal number of
detected photons during the sensor active time t.sub.A, called max
photon count (N.sub.max), is N.sub.max=t.sub.A/t.sub.D. For
example, with a sensor active time t.sub.A of 1 ms, and a dead time
of t.sub.D of 50 ns, the largest number of detected photons is
20,000. The Poisson noise yields a number of pulses
N.sub.Poisson.apprxeq. {square root over (N.sub.max)}. Without
accounting for the dark count rate which is negligible in the
present case (about 300 pulses per second, i.e. less than one pulse
per millisecond on average), we obtain a signal to noise ratio
SNR=20 log(N.sub.Max/ {square root over (N.sub.Max)})=20 log
{square root over (N.sub.Max)} (12)
[0104] In the example above, the signal to noise ratio is SNR=20
log {square root over (20000)}=43 dB.
[0105] This signal to noise ratio is high since, with an SPAD,
pulses are directly converted into TTL or CMOS compatible pulses
and counted. There is no need for an amplifier, a sampler and an
A/D converter which introduce additional noise. With a maximal
signal {square root over (N.sub.max)} times higher than the noise,
it becomes possible to build high-speed sensors capable of sensing
very low reflectances in the range between 1/100 and 1/1000, i.e.
corresponding to reflectance densities between 2 and 3.
[0106] The maximal photon count N.sub.max defines the size of the
counter (FIG. 3, 311) in terms of its number of bits. For
N.sub.max=20000, a 15 bits counter is sufficient. It is however
easy to reach N.sub.max=100,000 with a 17 bits counter or
N.sub.max=1,000,000 with a 24 bits counter.
Spectral Sensibilities of the Sensing System
[0107] If the illuminating device is white light, red, green, blue
or infra-red filter is placed within the light path, before the
corresponding sensor. In one embodiment, the red, green, and blue
filters have similar spectral sensibilities as the sensibilities
used for building densitometers, which are specified by DIN
standard 16536-2. The infra-red sensibilities should cover a part
of the near-infra-red spectrum, for example between 730 nm and 900
nm.
[0108] FIG. 6 shows the blue (B: 601), green (G: 602) and red (R:
603) normalized sensitivities according to DIN standard 16536-2
used for deriving the densities of the corresponding cyan, magenta
and yellow ink patches. These sensitivities correspond to the
multiplication of the sensibilities of filters in front of the
sensors and the own sensitivity of the sensors. In a possible
embodiment, these sensitivities, together with the near infra-red
sensitivity (IR: 604) can be used for the presently proposed 4
sensor sensing system.
[0109] If the illuminating devices are LEDs, then the light of the
chosen red, green blue and infra-red LEDs should be directed to the
print surface, be reflected and sensed by the corresponding sensor.
The sensor sensibilities should be known, and should provide a
positive response at the wavelengths of the blue, green, red and
infra-red LEDs, preferably located respectively between 380 nm and
480 nm, between 500 nm and 600 nm, between 620 and 720 nm and
between 750 nm and 900 nm. In the case that the sensibility of the
sensor is not given by the sensor manufacturer, it can be deduced
experimentally by irradiating the sensor with light at narrow
wavelengths, e.g. at each 10 nm between 380 nm and 900 nm, and by
measuring the corresponding responses.
Thickness Variations Deduced from Area Segments within a Printed
Sheet
[0110] The present invention aims at deducing ink thickness
variations at print time. Since during the print operation the
printed paper is moving forward at a given speed, we measure the
sensor responses over an area segment of the print along the paper
movement orientation. The position of the paper in respect to the
sensors is known at any time. The sensor acquisition logic may
acquire the sensor responses at regular time intervals. The sensor
responses from area segments known to be nearly uniform are
memorized and forwarded to the computing system which deduces the
ink thickness variations. As an alternative, by interacting with
the microcontroller 313 which drives the sensor acquisition logic
(FIG. 3B, 312), the software running on the computing system may
launch the sensor acquisition at a location on the print where the
area segment is nearly uniform.
[0111] When acquired from nearly uniform area segments, the sensor
responses integrated over an area segment are considered to be the
sensor responses of a uniform halftone patch whose nominal surface
coverages are the mean of the nominal surface coverages (e.g.
nominal c,m, y, and k values) of the corresponding area segment
pixels within the prepress sheet image.
[0112] The ink thickness variations, expressed by the ink thickness
variation factors, for the considered area segment are obtained by
minimizing a distance metric between the predicted area segment
sensor responses and the measured area segment sensor responses,
for example by minimizing the sum of square differences between the
predicted area segment sensor density responses and the measured
area segment sensor density responses. Accurate results which
reduce the impact of noise are obtained by measuring the sensor
responses at several area segment locations and by minimizing the
sum of these distances between predicted and measured sensor
responses at these locations.
[0113] In the case of the cyan, magenta, yellow and black inks, in
order to create for each ink an independent absorption wavelength
range, we consider wavelengths incorporating both the visible
wavelength range (380 nm to 730 nm) and the near-infra-red
wavelength range (e.g. 740 nm to 900 nm). In the near-infra-red
wavelength range, the cyan, magenta and yellow colorants do not
absorb light. Only the pigmented black ink absorbs light. An ink
thickness variation model with a wavelength range from 380 nm to
900 nm, i.e. with the visible and near-infra-red wavelength ranges,
enables computing ink thickness variations for the cyan, magenta,
yellow and black inks. FIG. 2 gives a schematic view of the ink
thickness variation computation system, for 4 inks I.sub.1,
I.sub.2, I.sub.3, and I.sub.4, which may represent the cyan,
magenta, yellow and black inks.
Normalized Ink Thickness Variation Computation
[0114] In the case of small variations between the calibration and
the printer operating conditions (e.g. the ink density during
calibration differs slightly from the ink density during normal
printing operation) more accurate results may be obtained by
computing normalized ink thickness variations.
[0115] Normalized ink thickness variation computation requires
establishing reference ink thickness variations dr.sub.1',
dr.sub.2', dr.sub.340 , dr.sub.4' (FIG. 2, 214) on a reference
print under reference settings of the printing device. In order to
create the reference settings, the printed color pictures are
observed and verified (e.g. compared with a soft proof on a
calibrated display) by a print operator. Alternately, it may be
possible to use another print verification system (see "Background
of the invention"). As soon as the current print result meets the
desired quality criteria (e.g. a color picture close to the desired
color picture or ink densities within a given tolerance range), the
reference ink thickness variations are deduced by measuring the
area segment sensor responses at different area segment locations,
by predicting the area segment sensor responses at these locations,
by deriving at each location according to a difference metric the
distance between measured and predicted area segment sensor
responses and by fitting the ink thickness variations so as to
minimize the sum of the computed distances across the considered
area segment locations. The fact that several color area segments
contribute to the computation of the reference ink thickness
variations reduces the impact of noise present within the
individual area segments.
[0116] From now on, ink thickness variation computations are
normalized in respect to these recorded reference thickness
variations dr.sub.1', dr.sub.2', dr.sub.3', dr.sub.4', i.e. the ink
thickness variation computing system computes the normalized ink
thickness variations dr.sub.1, dr.sub.2, dr.sub.3, dr.sub.4 in
respect to the initial ink thicknesses multiplied by the
corresponding reference ink thickness variations. FIG. 8A
illustrates normalized ink thickness variations 804 (axis 801)
deduced from polychromatic halftones present in printed sheets of
the magenta ink 801, and of the black ink (FIG. 8B, 814, axis 811)
for many different print trials, where the ink feed of one or of
several inks has been increased or decreased. In FIGS. 8A and 8B,
print trials 802 are characterized by a variation of the amount of
deposited ink. C+, M+, Y+, K+ indicate respectively a higher ink
feed of the cyan, magenta, yellow and black inks and C-, M-, Y-, K-
indicate respectively a lower ink feed of the cyan, magenta, yellow
and black inks. The labels "+", "-", "0" indicate respectively an
increment, a decrement or a constant value of the ink feed for the
considered ink (FIG. 8A: magenta ink, FIG. 8B: black ink). In order
to provide a comparison with the real amount of deposited ink, the
black triangles, placed according to the vertical axis on the right
of FIGS. 8A (803) and 8B (813), indicate the corresponding measured
relative scalar density values, i.e. the solid ink density values
measured on special patches located on the trial print sheet
divided by the solid ink density values measured on the reference
print sheet without any ink feed increase or decrease. The
reference print trial is located at the leftmost position. The gray
bars in FIGS. 8A and 8B indicate the range of values where no
significant ink thickness variations occur.
Ink Thickness Variation Computation in Respect to Reference
Settings
[0117] In a further embodiment, the system may track ink thickness
variations at print time without knowing the nominal surface
coverages of inks, but after having performed reference settings of
the print control parameters of the printing press (e.g. ink feed)
by an operator and/or by another print calibration system. Under
the reference settings, sensor responses (e.g. q.sub..alpha.',
q.sub..beta.', q.sub..gamma.', q.sub..delta.', FIG. 2, 215) are
measured from within specific area segments of a printed sheet.
Then, ink thickness variations occurring when printing that sheet
can be deduced by the ink thickness variation computing system.
[0118] The reference effective surface coverages and possibly
reference thickness variations are deduced from the reference
sensor responses and recorded. Then, while printing the same print
sheet, or when printing the same print sheet again in a new print
session, the corresponding sensor responses are measured. The ink
thickness variation computing system then computes the ink
thickness variations occurring in respect to the reference
settings. In the present embodiment, the ink thickness variation
computing system does not depend on the knowledge of nominal
surface coverages. It depends only on the initial calibration of
ink transmittances, of initial ink thicknesses and on the measured
sensor responses. Since only the effective surface coverages are
used, calibration is simplified by avoiding the need to establish
the effective surface coverage curves.
Ink Thickness Variation Computation
[0119] The method for computing ink thickness variations comprises
the step of calibration of a thickness variation and sensor
response enhanced spectral prediction model by (a) measuring the
paper reflectance, if applicable, deducing the internal paper
reflectance and deducing spectral ink transmittances from spectral
measurements, (b) computing the scalar ink thicknesses of the
superposed inks forming a solid colorant and (c) computing the
effective coverage curves for halftones in different superposition
conditions. Steps (b) and (c) can be performed either by spectral
measurements of by sensor responses.
[0120] In order to adapt an initial calibration to the current
print conditions (state of the printer, temperature, paper, inks),
the ink thickness variation computation method also comprises the
optional step of recalibrating at printing time the paper
reflectance and the effective surface coverage curves.
[0121] It further comprises the step of fitting, during print
operation, according to the thickness variation and sensor response
enhanced spectral prediction model, for each contributing ink, the
corresponding ink thickness variation factors. This is performed by
minimizing a distance metric such as the sum of square differences
between the predicted sensor responses and the measured sensor
responses. In the case of cyan, magenta, yellow and black inks, the
presently disclosed ink thickness variation computation method
works simultaneously within the visible and the near-infra-red
wavelength range domain.
[0122] In order to even better adapt the initial calibration to the
current print conditions (state of the printer, temperature, paper,
inks), an optional reference thickness variation computation step
enables computing reference thickness variations which are used for
computing normalized ink thickness variations during print
operation.
[0123] A further ink thickness variation computation method variant
also comprises, during online calibration, the step of measuring
reference sensor responses from specific locations of the print, of
deducing corresponding reference effective surface coverages and of
computing ink thickness variations by minimizing a distance metric
between the sensor responses predicted according to the thickness
variation enhanced spectral prediction model and the measured
sensor responses. This method does not need as calibration data the
effective surface coverage curves, but relies on the reference
sensor responses recorded under reference settings to compute the
reference effective surface coverages (see section "Ink thickness
variation computation in respect to reference settings").
Ink Thickness Variation Computing System
[0124] An ink thickness variation computing system is shown in FIG.
7, 710. It comprises a computing system 701 and a sensing system
708. The sensing system is made of an array 702 of illuminating 703
and sensor devices 704 located on the pathway of the printed paper
and of a processing module 706 for selecting, computing, storing
and delivering the sensor responses to the computing system 701.
The processing module receives from a subset of the lines 705 the
output of the sensor devices and optionally transmits through
another subset of the lines 705 sensor acquisition synchronization
signals. The sensing system's processing module 706 is connected to
the computing system 701 by a digital link 707, for example an USB
link.
[0125] According to the sensor responses, the computing system 701
computes the ink thickness variations by performing the steps
described in section "ink thickness variation computation". The
computing system can also perform the online calibration or
recalibration step of adapting the paper reflectance and the
surface coverage curves (see Section "Calibration of the ink
thickness variation computation model") according to the sensor
responses.
[0126] When connected to a print actuation parameter driving module
709, the ink thickness variation computing system 710 becomes an
online print parameter regulation system which regulates actuation
parameters such as the ink feed or the volume of deposited toner
per unit of time according to the current ink thickness
variations.
Specific Advantages of the Present Disclosure
[0127] Specific advantageous features of the presently disclosed
methods and systems are:
[0128] 1. The internal use of a spectral prediction model
incorporating explicitly ink thickness variations but without the
need of expensive online spectral reflectance measuring devices.
The only required spectral measurements are off-line one-time
initial calibration measurements for obtaining the paper
reflectance and the ink transmittances for a class of similar
papers and inks. This can be performed in the factory manufacturing
the printing device. During the print sessions, the only online
measurements are the sensor responses (or equivalently, the sensor
density responses), performed with non-expensive solid-state sensor
devices.
[0129] 2. The multi-channel sensor devices, for example 4 sensor
devices, are preferably embodied by Single Photon Avalanche Diodes
(SPADs). They allow building high-speed acquisition sensors with a
short active time (also called aperture time), typically between a
few hundreds of nanoseconds to a few milliseconds. They induce only
very low noise and require only simple digital electronics for
accumulating and counting the photon pulses in order to obtain the
reflected intensity.
[0130] 3. Due to the low price of the illuminating/sensor devices,
multi-channel sensors can be replicated over the width of the
printed sheet, allowing the acquisition of accurate ink thickness
information within the full printed sheet. In addition, their low
price enables using them within cheap mass product printers, such
as low cost ink-jet, dye-diffusion, thermal transfer and
electro-photographic printers, for the automatic regulation of
print activation parameters. Furthermore, since the presently
disclosed sensing system does not require any moving part, it is
also cheap for maintenance.
[0131] 4. Due to the short sensor acquisition times, in the order
of hundreds of nanoseconds to a few milliseconds, sensor responses
from short area segments (length: e.g. one to ten millimeters) can
be acquired, which incorporate a color halftone and no paper white.
This avoids the relatively important noise present in the paper
white reflectance and provides more robust results than accounting
for color variations within long stripe parts (long thin lines
along the length of the sheet) as proposed by U.S. Pat. No.
7,252,360 to Hersch et. al.
[0132] 5. In the present disclosure, we use the ink thickness and
sensor response enhanced spectral prediction model within a single
continuous wavelength range covering both the visible and the near
infra-red wavelength range. There is no need for two separate
applications of the model, one in the visible wavelength range and
one in the near infra-red wavelength range, as taught by U.S. Pat.
No. 7,252,360 to Hersch et. al. By operating over the visible and
near infra-red wavelength range, the sensor-based thickness
variation computation system can unambiguously compute simultaneous
thickness variations of the cyan, magenta, yellow and black
inks.
[0133] 6. The effective surface coverage curves expressing the
functions mapping nominal surface coverages into effective surface
coverages in the different superposition conditions, combined with
the spectral prediction model incorporating explicit transmittances
for all solid inks and with the formula modeling the sensor
responses from reflection spectra and from illuminating device
spectra (Equations 3) provide accurate sensor response
predictions.
[0134] 7. The calibration effort can be reduced in the case of
cyan, magenta, yellow and black inks by considering that the
superposition of an ink halftone and solid black yields black and
therefore does not have a direct impact on the mapping between
nominal and effective surface coverage of that ink halftone. This
allows reducing the number of curves mapping nominal to effective
surface coverages from 32 to 20.
[0135] 8. The ink thickness variation prediction model provides
improved thickness variation predictions thanks to the introduction
of an optional online refined calibration of the paper reflectance
and of the effective surface coverage curves performed during print
operation (running printer or printing press). The online
calibration is performed with multi-sensor responses on halftones
located within normal printed document pages.
[0136] 9. More accurate ink thickness variations are obtained by
measuring the sensor responses at several area segment locations
and by minimizing the sum of the differences (according to a
difference metric) between predicted and measured sensor responses
at these locations.
[0137] 10. When predicting ink thickness variations, we assume that
a small increase in the amount of ink does not change the ink
surface coverages. Therefore, fitting ink thickness variations from
sensor responses is the same as fitting ink volume variations.
However, in some printers, an increased ink thickness also yields
an increase of the corresponding effective surface coverages. Under
these conditions, with the methods described in the present
disclosure, we fit ink volume variations instead of ink thickness
variations. We may consider the deduced ink volume variations as a
multiplication of a pure ink surface variation factor and a pure
ink thickness variation factor. If we want to obtain the pure ink
thickness variation factor we may apply a function to the ink
volume variation factor, for example a square root function. But in
the general case, we can use directly the deduced ink volume
variations to regulate the print actuation parameters such as the
ink feed.
General Advantages
[0138] In addition to the specific advantages described above, the
invention has also similar advantages as taught by U.S. Pat. No.
7,252,360 to Hersch et. al.:
[0139] 1. The ink thickness variations which have been introduced
into the spectral prediction model are exactly the variables needed
to control the ink deposition process within a printing press or a
printer.
[0140] 2. The fact that ink thickness variations of the
contributing inks can be computed from sensor measurements at
various locations within the printed document pages enables
avoiding printing special patches or control strips at the border
of the printed sheet and therefore also avoids the need to cut
these elements out after printing.
[0141] 3. In case that the calibration conditions deviate slightly
from the normal print operating conditions, a recorded set of
reference ink thickness variations enables deducing during print
operation normalized ink thickness variations with an improved
precision.
[0142] 4. Ink thickness variations may also be computed, when the
nominal surface coverages of the target halftone area segment are
unknown, by measuring under reference settings, for a halftone area
segment, reference sensor values, by deriving a corresponding set
of reference effective surface coverages and by computing for the
same halftone area segment in the following printed sheets, the ink
thickness variations by minimizing a distance metric between the
sensor values predicted according to the reference effective
surface coverages and the currently measured sensor values.
[0143] The disclosed simple and non-expensive solid device sensing
system make ink thickness variation prediction and therefore the
regulation of print actuation variables applicable to many
different kinds of printing devices, from expensive large format
printing devices to small and cheap printing devices.
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