U.S. patent application number 09/073873 was filed with the patent office on 2001-11-22 for image processing system for compressing image data including binary image data and continuous tone image data by a sub-band transform method with a high-compression rate.
Invention is credited to MATSUURA, NEKKA, YAGISHITA, TAKAHIRO, YAMAZAKI, YUKIKO.
Application Number | 20010043752 09/073873 |
Document ID | / |
Family ID | 27313525 |
Filed Date | 2001-11-22 |
United States Patent
Application |
20010043752 |
Kind Code |
A1 |
MATSUURA, NEKKA ; et
al. |
November 22, 2001 |
IMAGE PROCESSING SYSTEM FOR COMPRESSING IMAGE DATA INCLUDING BINARY
IMAGE DATA AND CONTINUOUS TONE IMAGE DATA BY A SUB-BAND TRANSFORM
METHOD WITH A HIGH-COMPRESSION RATE
Abstract
An image processing system which compresses an image including
both a binary image and a continuous tone image by a sub-band
transform method with a high compression rate. A 2.times.2 pixel
matrix block is extracted from image data. A transform factor
having a plurality of frequency components is obtained from the
2.times.2 pixel matrix block data. The transform factor is
quantized by a fixed-length quantizing method by deleting a
predetermined number of lower order bits of each of the frequency
components.
Inventors: |
MATSUURA, NEKKA; (TOKYO,
JP) ; YAMAZAKI, YUKIKO; (KANAGAWA, JP) ;
YAGISHITA, TAKAHIRO; (KANAGAWA, JP) |
Correspondence
Address: |
OBLON SPIVAK MCCLELLAND MAIER & NEUSTADT PC
FOURTH FLOOR
1755 JEFFERSON DAVIS HIGHWAY
ARLINGTON
VA
22202
US
|
Family ID: |
27313525 |
Appl. No.: |
09/073873 |
Filed: |
May 7, 1998 |
Current U.S.
Class: |
382/248 |
Current CPC
Class: |
G06T 9/007 20130101;
G06T 9/005 20130101 |
Class at
Publication: |
382/248 |
International
Class: |
G06K 009/36; G06K
009/46 |
Foreign Application Data
Date |
Code |
Application Number |
May 8, 1997 |
JP |
9-118207 |
May 29, 1997 |
JP |
9-156006 |
May 29, 1997 |
JP |
9-156007 |
Claims
What is claimed is:
1. An image processing system comprising: a buffer unit extracting
n.times.m pixel matrix block data from image data, where n and m
are integers; a sub-band transform unit transforming the n.times.m
pixel matrix block data by a sub-band transform method so as to
obtain a transform factor having a plurality of frequency
components; and a quantizing unit quantizing the transform factor
by a fixed-length quantizing method by deleting a predetermined
number of lower order bits of each of the frequency components.
2. An image processing system comprising: a buffer unit extracting
n.times.m pixel matrix block data from image data, where n and m
are integers; a sub-band transform unit transforming the n.times.m
pixel matrix block data by a sub-band transform method so as to
obtain a transform factor having a plurality of frequency
components including a low-frequency component and a high-frequency
component; an area discriminating unit discriminating a type of an
image area corresponding to the n.times.m pixel matrix block data
being processed so that the image area is determined as one of an
edge area and a non-edge area, a discrimination being made based on
whether or not an absolute value of each of the components of the
transform factor exceeds a threshold value; and a quantizing unit
quantizing the transform factor by a fixed-length quantizing method
by deleting a predetermined number of lower order bits of each of
the frequency components, the number of deleted lower order bits of
each of the frequency components of the transform factor being
changed in accordance with a type of image area being processed so
that image data including the transform factor and flag information
indicating a type of image area has a predetermined fixed
length.
3. The image processing system as claimed in claim 2, wherein said
quantizing unit deletes lower order bits of each of the
low-frequency component and the high-frequency component so that a
number of deleted lower order bits for the edge area is greater
than a number of deleted lower order bits for the non-edge
area.
4. The image processing system as claimed in claim 2, wherein said
quantizing unit quantizes the high-frequency component by a vector
quantizing method.
5. The image processing system as claimed in claim 2, wherein said
quantizing unit embeds the flag information into the transform
factor.
6. The image processing system as claimed in claim 5, wherein said
quantizing unit changes bit data representing the transform factor
so that the flag information is represented by a part of the data
bits representing the transform factor.
7. The image processing system as claimed in claim 2, wherein said
quantizing unit changes the transform factor so that correlation
between the transform factors of different types is increased.
8. The image processing system as claimed in claim 7, wherein said
quantizing unit changes a bit arrangement of the transform factor
so that correlation between the transform factor corresponding to
the edge area and the transform factor corresponding to the
non-edge area is increased.
9. An image processing method comprising the steps of: extracting
n.times.m pixel matrix block data from image data, where n and m
are integers; transforming the n.times.m pixel matrix block data by
a sub-band transform method so as to obtain a transform factor
having a plurality of frequency components; and quantizing the
transform factor by a fixed-length quantizing method by deleting a
predetermined number of lower order bits of each of the frequency
components.
10. An image processing method comprising the steps of: extracting
n.times.m pixel matrix block data from image data, where n and m
are integers; transforming the n.times.m pixel matrix block data by
a sub-band transform method so as to obtain a transform factor
having a plurality of frequency components including a
low-frequency component and a high-frequency component;
discriminating a type of an image area corresponding to the
n.times.m pixel matrix block data being processed so that the image
area is determined as one of an edge area and a non-edge area, a
discrimination being made based on whether or not an absolute value
of each of the components of the transform factor exceeds a
threshold value; and quantizing the transform factor by a
fixed-length quantizing method by deleting a predetermined number
of lower order bits of each of the frequency components, the number
of deleted lower order bits of each of the frequency components of
the transform factor being changed in accordance with a type of
image area being processed so that image data including the
transform factor and flag information indicating a type of image
area has a predetermined fixed length.
11. An image processing system comprising: a buffer unit extracting
n.times.m pixel matrix block data from image data, where n and m
are integers; a binarizing unit transforming the n.times.m pixel
matrix block data into binary data represented by a maximum value
and a minimum value; a differential data calculating unit
calculating differential data which is a difference between a value
of each pixel in the n.times.m pixel matrix block data and one of
the maximum value and the minimum value of the binary data; a
sub-band transform unit transforming the differential data by a
sub-band transform method so as to obtain a transform factor having
a plurality of frequency components; and an encoding unit encoding
the binary data and the sub-band transform factor so as to obtain a
code representing the image data.
12. The image processing system as claimed in claim 11, wherein
said encoding unit deletes lower order bits of the sub-band
transform factor so that the code has a predetermined fixed
length.
13. The image processing system as claimed in claim 12, wherein
said encoding unit deletes more lower order bits from the
high-frequency component than the low-frequency component when both
the maximum value and the minimum value exist in the binary data of
the same block data.
14. The image processing system as claimed in claim 11, wherein
said encoding unit quantizes the high-frequency component of the
sub-band transform factor by a vector quantizing method.
15. An image processing method comprising the steps of: extracting
n.times.m pixel matrix block data from image data, where n and m
are integers; transforming the n.times.m pixel matrix block data
into binary data represented by a maximum value and a minimum
value; calculating differential data which is a difference between
a value of each pixel in the n.times.m pixel matrix block data and
one of the maximum value and the minimum value of the binary data;
transforming the differential data by a sub-band transform method
so as to obtain a transform factor having a plurality of frequency
components; and encoding the binary data and the sub-band transform
factor so as to obtain a code representing the image data.
16. An image processing system comprising: a dividing unit dividing
image data into a plurality of n.times.m pixel matrix block data,
where n and m are integers; a transform unit transforming each
pixel in the n.times.m pixel matrix block data by a frequency
transform method so as to produce a transform factor including a
high-frequency component and a low-frequency component; an image
area discriminating unit for determining whether the block being
processed corresponds to an edge area or a non-edge area based on
the transform factor output from said transform unit; a quantizing
unit quantizing the transform factor for the edge area and the
transform factor for the non-edge area by different methods; and an
encoding unit encoding an output of said quantizing unit by an
entropy encoding method, wherein a total of a number of bits of the
high-frequency component and a number of bits of the low-frequency
is the same regardless of types of the edge area or the non-edge
area, and a number of bits of the high-frequency component for the
edge area is the same as a number of bits of the low-frequency
component of the non-edge area.
17. The image processing system as claimed in claim 16, wherein
said encoding unit encodes error data generated by said quantizing
unit.
18. The image processing system as claimed in claim 16, wherein an
encoding of the image for the edge area is performed by using only
the high-frequency component, and an encoding of the image for the
non-edge area is performed by using only the low-frequency
component.
19. The image processing system as claimed in claim 16, wherein
every other block data is used for restoring an original image.
20. An image processing method comprising the steps of: dividing
image data into a plurality of n.times.m pixel matrix block data,
where n and m are integers; transforming each pixel in the
n.times.m pixel matrix block data by a frequency transform method
so as to produce a transform factor including a high-frequency
component and a low-frequency component; determining whether the
block being processed corresponds to an edge area or a non-edge
area based on the transform factor output from said transform unit;
quantizing the transform factor for the edge area and the transform
factor for the non-edge area by different methods; and encoding an
output of said quantizing unit by an entropy encoding method,
wherein a total of a number of bits of the high-frequency component
and a number of bits of the low-frequency is the same regardless of
types of the edge area or the non-edge area, and a number of bits
of the high-frequency component for the edge area is the same as a
number of bits of the low-frequency component of the non-edge area.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention generally relates to an image data
compressing technique and, more particularly, to an image
processing system which compresses and expands image data by using
a sub-band encoding method.
[0003] The image processing system related to the present invention
may be used in a digital copy machine, a facsimile machine, a
digital printer, a digital camera or a digital video camera, and
also may be used in an image recording system such as a CD-ROM
drive or a floppy disc drive.
[0004] 2. Description of the Related Art
[0005] A sub-band encoding method such as the discrete cosine
transform (DCT) or the Harr Wavelet transform is considered a
method for effectively compressing a continuous tone image.
Additionally, Japanese Laid-Open Patent Application No. 2-305272
discloses another method for encoding image data by separating an
image area into a character area and a halftone area so as to
encode these areas by an encoding method appropriate for each of
the areas.
[0006] Such a method for compressing image data using the sub-band
transform such as the DCT or the Harr Wavelet transform can
effectively compress a continuous tone image. However, there is a
problem in that a compression rate is low when a complete binary
image is compressed.
[0007] Additionally, in a digital copy machine, even if an original
image is a complete binary image, image data obtained by scanning
such a complete binary image may become incomplete binary image
data due to fluctuation in a scanning operation. Thus, there may be
a problem in compressing such incomplete binary data by an
entropy-encoding method due to fluctuation in a scanned image.
[0008] As a method for rotating or sorting images in a copy
machine, a block truncation encoding (BTC) which is one of fixed
length encoding methods is popular. However, there is a problem in
that a compression rate for an entropy encoding is low as compared
to that of a sub-band transform method, and a calculation is
complex.
[0009] In an image forming apparatus such as a copy machine or a
printer, image data obtained by a scanner is subjected to gamma
correction or a filtering process so as to adjust image quality.
The thus-processed image data is stored in a memory, and then the
image data is sent to a printing unit.
[0010] Generally, such image data is subjected to a data
compression in order to reduce a capacity of the memory that stores
the processed image data. Generally, in a data compressing method,
image data is transformed into frequency components by using an
orthogonal transformation such as the discrete cosine transform
(DCT), and the quantized image data is subjected to an entropy
encoding. Dispersion of a high-frequency factor in the frequency
transformation factors varies in response to a magnitude of change
in intensity of the image. Thus, the image quality is improved when
a quantizing method is changed in response to a type of an area to
be processed.
[0011] Japanese Laid-Open Patent Application No. 7-74959 discloses
a technique in which a quantization table is changed based on a
transform factor obtained by an orthogonal transformation of an
original image by each individual block so that the image quality
matches the contents of the image data and a compression rate is
improved.
[0012] When an image is printed by a copy machine, a character
image and a line image can be well recognized by rendering the
intensity slope of a contour of the characters or the lines to be
steep. On the other hand, when an image having a gentle intensity
slope such as a photograph is printed, a random change in the
intensity having a small amplitude is sensed as a noise. Thus, it
is preferred for such a photographic image to reduce the intensity
slope of an output image. Particularly, in a mesh point
photographic image, a better image quality can be obtained by
reducing the intensity slope even for an area having a steep
intensity slope.
[0013] Accordingly, an edge area corresponding to a character image
or a line image is separated from a mesh point image and a gentle
slope area of a photographic image so that the edge area is
subjected to a differential filtering process whereas the
photographic image is subjected to a smoothing filtering process.
Additionally, when an image data compression is performed, another
separation of image areas is performed in response to degrees of
the intensity slope in edge areas.
[0014] As mentioned above, in the conventional technique, two
separation processes are performed on the same image data and the
filtering process is performed separately from the quantizing
process. Thus, there is a problem in that a process time is
increased and a hardware cost is increased. Additionally, there is
a disadvantage in the technique disclosed in the above-mentioned
patent document in that a compression rate is not minimized since a
result of the area separation must be also stored as the compressed
data.
[0015] An image data compression technique is generally used in the
image data processing field so as to reduce a capacity of a memory
for storing image data or reduce a time for transmitting image
data. There are various image data compressing methods depending on
the processing modes of image data. When image data is printed, a
rotation of the image may be requested. In order to rotate the
image at a high speed, a fixed length compression is used.
[0016] Additionally, when image data is exchanged between systems
having different resolutions or gradation characteristics, a
compressing method using a layered data structure is desired so as
to select transmission data corresponding to an image quality of an
image outputting system. Especially, when image data is transmitted
to a display apparatus, a progressive transmission method is
required. In the progressive transmission method, image data of an
object such as an icon can be transmitted prior to sending the
image data. Thus, data compression is performed in response to the
level of layers.
[0017] Additionally, when a trial printing is performed for
checking a layout while reducing toner consumption in an image
printing apparatus, a data compressing method is required by which
a feature of the image is maintained but image quality is not
reduced.
[0018] Japanese Laid-Open Patent Application No. 1-135265 discloses
a data compressing method in which an original image is divided
into a plurality of blocks, and each block is divided into image
data which is orthogonal-transformed and other data so that a
representative image of the image file can be effectively
regenerated.
[0019] Generally, an image comprises an image area and an edge
area. In the image area, a gradation of the image gradually
changes, such as in a photograph or a graphic image. In the edge
area, a gradation sharply changes in an area of an edge of the
image and an area adjacent to the edge, such as in a character
image or a line image. When the visual sense of human beings is
considered, gradation is important in the image area whereas
resolution is important in the edge area.
[0020] In the conventional technique disclosed in the
above-mentioned patent document, a sampling is performed on the
original image, and the sampled image data is subjected to the
discrete cosine transform (DCT). The same transform factor which is
obtained from a quantization table is used for all areas. In such a
case, a length of data is fixed since a single quantization table
is used. However, there is a drawback in that the quantized image
data does not accurately represent the feature of the image while a
large amount of data is used since a single quantization is used.
Additionally, there is a disadvantage in that only two levels of
image data can be selected.
SUMMARY OF THE INVENTION
[0021] It is a general object of the present invention to provide
an improved and useful image processing system in which the
above-mentioned problems are eliminated.
[0022] A more specific object of the present invention is to
provide an image processing system which compresses an image
including both a binary image and a natural image by a sub-band
transform method with a high compression rate.
[0023] Another object of the present invention is to provide an
image processing system which facilitates processing and editing of
image data by using a sub-band transform method or a fixed length
encoding method at a low cost.
[0024] A further object of the present invention is to provide an
image processing system which can represent a feature of an image
while a reduced amount of data is used, and which can produce image
data including a plurality of levels of image quality.
[0025] In order to achieve the above-mentioned objects, there is
provided according to one aspect of the present invention an image
processing system comprising:
[0026] a buffer unit extracting n.times.m pixel matrix block data
from image data, where n and m are integers;
[0027] a sub-band transform unit transforming the n.times.m pixel
matrix block data by a sub-band transform method so as to obtain a
transform factor having a plurality of frequency components;
and
[0028] a quantizing unit quantizing the transform factor by a
fixed-length quantizing method by deleting a predetermined number
of lower order bits of each of the frequency components.
[0029] According to the above-mentioned invention, since the
high-frequency component of the sub-band transform factor is
quantized by deleting the Lower order bits in the fixed-length
quantizing method, various subsequent processes such as editing of
the image or a rotation of the image can be easily performed with a
reduced amount of compressed data.
[0030] Additionally, there is provided according to another aspect
of the present invention an image processing system comprising:
[0031] a buffer unit extracting n.times.m pixel matrix block data
from image data, where n and m are integers;
[0032] a sub-band transform unit transforming the n.times.m pixel
matrix block data by a sub-band transform method so as to obtain a
transform factor having a plurality of frequency components
including a low-frequency component and a high-frequency
component;
[0033] an area discriminating unit discriminating a type of an
image area corresponding to the n.times.m pixel matrix block data
being processed so that the image area is determined as one of an
edge area and a non-edge area, a discrimination being made based on
whether or not an absolute value of each of the components of the
transform factor exceeds a threshold value; and
[0034] a quantizing unit quantizing the transform factor by a
fixed-length quantizing method by deleting a predetermined number
of lower order bits of each of the frequency components, the number
of deleted lower order bits of each of the frequency components of
the transform factor being changed in accordance with a type of
image area being processed so that image data including the
transform factor and flag information indicating a type of image
area has a predetermined fixed length.
[0035] According to this invention, a number of the lower order
bits of each of the frequency components of the transform factor is
deleted in response to a type of the image, and the transform
factor is quantized by a fixed-length quantizing method together
with the flag information. Thus, the image data can be efficiently
compressed with a high quality irrespective of whether the image
data corresponds to an edge area or a non-edge area. Additionally,
the original image data can be easily restored on a decoder side
based on the flag information.
[0036] In the above-mentioned invention, the quantizing unit may
delete lower order bits of each of the low-frequency component and
the high-frequency component so that a number of deleted lower
order bits for the edge area is greater than a number of deleted
lower order bits for the non-edge area. Accordingly, data
corresponding to both the edge area in which a gradation is
important and the non edge area in which recognition of an edge is
important can be efficiently compressed while a high image quality
is maintained. Additionally, the original image data can be easily
restored on a decoder side based on the flag information.
[0037] Additionally, the quantizing unit may quantize the
high-frequency component by a vector quantizing method. Further,
the quantizing unit may embed the flag information into the
transform factor.
[0038] Additionally, the quantizing unit may change bit data
representing the transform factor so that the flag information is
represented by a part of the data bits representing the transform
factor. The quantizing unit may change the transform factor so that
correlation between the transform factors of different types is
increased. The quantizing unit may change a bit arrangement of the
transform factor so that correlation between the transform factor
corresponding to the edge area and the transform factor
corresponding to the non-edge area is increased.
[0039] Additionally, there is provided according to another aspect
of the present invention an image processing system comprising:
[0040] a buffer unit extracting n.times.m pixel matrix block data
from image data, where n and m are integers;
[0041] a binarizing unit transforming the n.times.m pixel matrix
block data into binary data represented by a maximum value and a
minimum value;
[0042] a differential data calculating unit calculating
differential data which is a difference between a value of each
pixel in the n.times.m pixel matrix block data and one of the
maximum value and the minimum value of the binary data;
[0043] a sub-band transform unit transforming the differential data
by a sub-band transform method so as to obtain a transform factor
having a plurality of frequency components; and
[0044] an encoding unit encoding the binary data and the sub-band
transform factor so as to obtain a code representing the image
data.
[0045] According to the above-mentioned invention, a continuous
tone image data can be efficiently compressed by a sub-band
transform since the original image data is represented by using the
binary data and the sub-band transform factor which are encoded by
a sub-band transform. Accordingly, an image including a binary
image and a continuous tone image can be processed by a single
method irrespective of types of the image.
[0046] In the above-mentioned invention, the encoding unit may
delete lower order bits of the sub-band transform factor so that
the code has a predetermined fixed length. Accordingly, the image
data including the binary image data and the continuous tone image
data can be compressed with a high compression rate while a high
image quality is maintained. The compression rate is higher than
that of the block truncation encoding method.
[0047] Additionally, the encoding unit may delete a greater number
of lower order bits from the high-frequency component than the
low-frequency component when both the maximum value and the minimum
value exist in the binary data of the same block data. Accordingly,
a change in an average intensity in a block having a sharp
gradation change can be prevented, resulting in a prevention of
deterioration of the image quality.
[0048] Further, the encoding unit may quantize the high-frequency
component of the sub-band transform factor by a vector quantizing
method.
[0049] Additionally, there is provided according to another aspect
of the present invention an image processing system comprising:
[0050] a dividing unit dividing image data into a plurality of
n.times.m pixel matrix block data, where n and m are integers;
[0051] a transform unit transforming each pixel in the n.times.m
pixel matrix block data by a frequency transform method so as to
produce a transform factor including a high-frequency component and
a low-frequency component;
[0052] an image area discriminating unit for determining whether
the block being processed corresponds to an edge area or a non-edge
area based on the transform factor output from the transform
unit;
[0053] a quantizing unit quantizing the transform factor for the
edge area and the transform factor for the non-edge area by
different methods; and
[0054] an encoding unit encoding an output of the quantizing unit
by an entropy encoding method,
[0055] wherein a total of a number of bits of the high-frequency
component and a number of bits of the low-frequency is the same
regardless of types of the edge area or the non-edge area, and a
number of bits of the high-frequency component for the edge area is
the same as a number of bits of the low-frequency component of the
non-edge area.
[0056] According to the above-mentioned embodiment, image data
representing a feature of the original image can be produced while
an amount of data is reduced. Additionally, image data
corresponding to a plurality of image quality levels can be
produced.
[0057] In the above-mentioned invention, the encoding unit may also
encode error data generated by the quantizing unit. Accordingly,
the restored image data can almost completely match the original
data.
[0058] Additionally, an encoding of the image for the edge area may
be performed by using only the high-frequency component, and an
encoding of the image for the non-edge area is performed by using
only the low-frequency component. According to this invention, the
image quality of the restored image may be low, but a feature of
the original image can be sufficiently maintained.
[0059] Further, in the above-mentioned invention, every other block
data may be used for restoring an original image. According to this
invention, the image quality of the restored image may be low, but
a feature of the original image can be sufficiently maintained
while an amount of data is reduced. Thus, a reduced-size image can
be easily obtained.
[0060] Other objects, features and advantages of the present
invention will become more apparent from the following detailed
descriptions when read in conjunction with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0061] FIG. 1 is a block diagram of an image processing system
according to a first embodiment of the present invention;
[0062] FIG. 2 is an illustration for explaining a sub-band
transformation unit shown in FIG. 1;
[0063] FIG. 3 is an illustration for explaining a compressing and
expanding operation performed by the image processing system shown
in FIG. 1;
[0064] FIG. 4A is an illustration for explaining a compressing
operation performed by the image processing system shown in FIG. 1
when a complete binary image fluctuates; FIG. 4B is an illustration
for explaining an expanding operation for data obtained by the
compressing operation of FIG. 4A;
[0065] FIG. 5 is an illustration for explaining an operation
performed by the image processing system shown in FIG. 1 for
deleting lower order bits;
[0066] FIG. 6 is an illustration for explaining an operation
performed by the image processing system shown in FIG. 1 for
deleting lower order bits;
[0067] FIG. 7 is an illustration of a quantization table;
[0068] FIG. 8 is a block diagram of an image processing system
according to a second embodiment of the present invention;
[0069] FIG. 9 is an illustration for explaining a transformation of
image data performed by the image processing system shown in FIG.
8
[0070] FIG. 10 is a block diagram of an image processing system
according to a third embodiment of the present invention;
[0071] FIG. 11 is an illustration for explaining a compressing and
expanding operation performed by the image processing system shown
in FIG. 10;
[0072] FIG. 12 is a block diagram of an image processing system
according to a fourth embodiment of the present invention;
[0073] FIG. 13 is an illustration for explaining an operation of
the image processing system shown in FIG. 12;
[0074] FIG. 14A is an illustration for explaining a compressing and
expanding operation for a non-edge area performed by the image
processing system shown in FIG. 12; FIG. 14B is an illustration for
explaining a compressing and expanding operation for an edge area
performed by the image processing system shown in FIG. 12;
[0075] FIG. 15 is a block diagram of an image processing system
according to a fifth embodiment of the present invention;
[0076] FIG. 16 is an illustration for explaining an operation of
the image processing system shown in FIG. 15;
[0077] FIG. 17A is an illustration for explaining a bit assignment
for an edge area in the fifth embodiment; FIG. 17B is an
illustration for explaining a bit assignment for a non-edge area in
the fifth embodiment;
[0078] FIG. 18A is an illustration for explaining a compressing and
expanding operation for a non-edge area performed by the image
processing system according to the fifth embodiment of the present
invention:; FIG. 18B is an illustration for explaining a
compressing and expanding operation for an edge area performed by
the image processing system according to the fifth embodiment of
the present invention;
[0079] FIG. 19 is a block diagram of an image processing system
according to a sixth embodiment of the present invention;
[0080] FIG. 20 is an illustration for explaining a compressing and
expanding operation performed by the image processing system shown
in FIG. 19;
[0081] FIG. 21 is an illustration for explaining an operation
performed by an image processing system according to a seventh
embodiment of the present invention;
[0082] FIG. 22A is an illustration for explaining a bit assignment
for a non-edge area of the seventh embodiment; FIG. 22B is an
illustration for explaining a bit assignment for an edge area of
the seventh embodiment; FIG. 22C is an illustration for explaining
discrimination of the non-edge area and the edge-area; FIG. 22D is
an illustration of a vector quantization table used in the seventh
embodiment;
[0083] FIG. 23A is an illustration for explaining a compressing
operation performed in the seventh embodiment; FIG. 23B is an
illustration for explaining an enlarging operation performed in the
seventh embodiment; and
[0084] FIG. 24A is an illustration for explaining a bit assignment
for an edge area of the seventh embodiment; FIG. 24B is an
illustration for explaining a bit assignment for a non-edge of the
seventh embodiment; FIG. 24C is an illustration for explaining a
bit assignment for an edge area of an eighth embodiment; FIG. 24D
is an illustration for explaining a bit assignment for a non-edge
of the eighth embodiment.
[0085] FIG. 25 is a block diagram of an image processing system
according to a ninth embodiment of the present invention;
[0086] FIG. 26 is a circuit diagram of a wavelet transform unit
shown in FIG. 25;
[0087] FIG. 27 is an illustration for explaining a pixel block and
a frequency transform factor;
[0088] FIG. 28 is a graph showing a quantization
characteristic;
[0089] FIG. 29A is an illustration for explaining quantization
representative values and ranges defined by threshold values when a
gain of a quantizing unit is 2.0; FIG. 29B is an illustration for
explaining quantization representative values and ranges defined by
threshold values when a gain of a quantizing unit is 1.0; FIG. 29C
is an illustration for explaining quantization representative
values and ranges defined by threshold values when a gain of a
quantizing unit is 0.5;
[0090] FIG. 30A is an illustration of high-frequency components for
an edge area; FIG. 30B is an illustration of high-frequency
components for a non-edge area; FIG. 30C is an illustration of
high-frequency components for an edge area in which two
high-frequency components have large values;
[0091] FIG. 31 is a block diagram of an image processing system
according to a tenth embodiment of the present invention;
[0092] FIGS. 32A is an illustration showing an example of a
two-dimensional vector quantization using 7 quantization values;
FIGS. 32B is an illustration showing an example of a
two-dimensional vector quantization using 15 quantization
values;
[0093] FIG. 33 is an illustration for explaining an example of a
bit arrangement for each area; and
[0094] FIG. 34 is an illustration of a part of an original
image.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0095] A description will now be given, with reference to FIGS. 1
through 6, of a first embodiment of the present invention. FIG. 1
is a block diagram of an image processing system according to the
first embodiment of the present invention. FIG. 2 is an
illustration for explaining a sub-band transformation unit shown in
FIG. 1. FIG. 3 is an illustration for explaining a compressing and
expanding operation performed by the image processing system shown
in FIG. 1. FIGS. 4A and 4B are illustrations for explaining a
compressing and expanding operation performed by the image
processing system shown in FIG. 1 when a complete binary image
fluctuates. FIGS. 5 and 6 are illustrations for explaining an
operation performed by the image processing system shown in FIG. 1
for deleting lower order bits.
[0096] The image processing system according to the first
embodiment is applied to a printer using 8-bit data (256 gradation
levels) for representing image data. The image processing system
shown in FIG. 1 comprises a buffer unit 201, a binary image
differential data producing unit 202, a sub-band transformation
unit 203, entropy-encoding units 204a and 204b and a memory
205.
[0097] The buffer unit 201 extracts 2.times.2 matrix pixels from
image data, and stores the 2.times.2 matrix pixel data therein. The
2.times.2 matrix pixel data stored in the buffer unit 201 is
transformed into binary image data and differential data by the
binary image differential data producing unit 202. The binary data
in the present embodiment is obtained by transforming the image
data having intensity values from "0" through "255" into one of
binary values "255" and "0". That is, the image data having an
intensity value equal to or greater than "128" is transformed into
the value "255" and the image data having an intensity value
smaller than "128" is transformed into the value "0". It should be
noted that the binary values "255" and "0" are represented by
values "1" and "0", respectively.
[0098] The differential data represents an absolute value of a
difference between the original image data and one of the binary
values "255" and "0". The data having an intensity value equal to
or greater than "128" is calculated by the following equation.
(differential data)=(binary data "255")-(intensity value)
[0099] The data having an intensity value smaller than "128" is
calculated by the following equation.
(differential data)=(intensity value)-(binary data "0")
[0100] When a reverse transformation is performed, the data having
an intensity value equal to or greater than "128" is calculated by
the following equation.
(reverse transform value)=(binary data "255")-(differential
value)
[0101] The data having an intensity value smaller than "128" is
calculated by the following equation.
(reverse transform value)=(differential value)-(binary data
"0")
[0102] The binary data "255" and "0" are then encoded by using an
entropy encoding by the entropy-encoding unit 104a. The encoded
data is stored in the memory 205. On the other hand, the
differential data is transformed by using a sub-band transform
method such as the Harr Wavelet transform method by the sub-band
transform unit 203. Then, the transformed data is encoded by using
the entropy-encoding by the entropy encoding unit 204b. The encoded
differential data is also stored in the memory 205.
[0103] In this embodiment, the sub-band transform unit 203
transforms the differential data of pixels a through d shown in
FIG. 2 by using the Harr Wavelet transform method so as to obtain a
low frequency component LL and high-frequency components HL, LH and
HH as shown in FIG. 2. At this time, decimals are omitted.
LL={(a+b)/2+(c+d)/2}/2
HL={(a-b)+(c-d)}/2
LH={(a+b)-(c+d)}/2
HH=(a-b)-(c-d) (1)
[0104] The differential data between the original image data and
one of the binary data "255" and "0" takes a value from "0" to
"127". The LL component is represented by 7-bit data since it takes
a value from "0" to "127". Each of the HL component and the LH
component is represented by 8-bit data since it takes a value from
"-127" to "127". The HH component is represented by 9-bit data
since it takes a value from "-255" to "255". When the reverse
sub-band transform is performed, the original image data of the
pixels a through d are restored by using the LL, HL, LH, HH
components and the above equations (1).
[0105] A description will now be given, with reference to FIG. 3,
of an example of a process performed by the image processing system
shown in FIG. 1. It is assumed that original image data 301
including the pixels a through d is input to the image processing
system, and the pixels a through d have the following intensity
values.
[0106] a=175
[0107] b=0
[0108] c=175
[0109] d=20
[0110] In this case, the binary data of the pixels a through d are
represented as follows.
[0111] a=255="1"
[0112] b=0="0"
[0113] c=255="1"
[0114] d=0="0"
[0115] Additionally, the differential data 302 of the pixels a
through d is represented as follows.
[0116] a=255-175=80
[0117] b=0-0=0
[0118] c=255-175=80
[0119] d=20-0=20
[0120] The differential data 302 is transformed by the sub-band
transform method, and the following data 303 is obtained.
[0121] LL=45
[0122] HL=70
[0123] LH=-10
[0124] HH=20
[0125] The data 303 is temporarily stored in a memory 304. The data
303 is read to obtain the original differential data 302 by
performing a reverse sub-band transform based on the equations (1).
Additionally, the differential data 302 can be calculated based on
the binary data "0" and "1".
[0126] a=255-80=175
[0127] b=0-0=0
[0128] c=255-80=175
[0129] d=20-0=20
[0130] Accordingly, the original data is restored.
[0131] When a continuous tone image is compressed, an amount of
information of binary data after compression or an amount of
information of sub-band transform factors after compression is
negligibly small. This is because the binary data of the continuous
tone image is a simple binary image, and it can be compressed into
very small amount. Additionally, the differential data (sub-band
transform factors after compression) is close to "0".
[0132] A description will now be given of a case of a digital copy
machine in which image data corresponding to a single page is
compressed based on a fixed-length compression method, and is
stored in a memory (hereinafter referred to as a page memory) for
processing and editing (90-degree rotation) the image. With respect
to a quantization, as shown in FIGS. 4A and 4B, the LL component is
represented by a multiple of "4": each of the HL and LH components
is represented by a multiple of "16"; and the HH component is
represented by a multiple of "64". That is, when the quantization
is performed, the LL component is divided by "4" (two lower order
bits are deleted); each of the HL and LH components is divided by
"16" (four lower order bits are omitted); and the HH component is
divided by "64" (six lower order bits are deleted). According to
the above-mentioned quantization, the LL component (0 to 255) can
be represented by 6-bit data; each of the HL and LH components
(-255 to 255) can be represented by 5-bit data; and the HH
component (-510 to 510) can be represented by 4-bit data. The total
number of the data bits is 20 bits.
[0133] When the copy machine scans an original image, complete
binary image data cannot be obtained due to fluctuation in an
intensity of the image, the complete binary image data comprising
only the minimum values "0" and the maximum values "255". FIGS. 4A
and 4B shows a process for compressing and expanding original image
data 401 which is not the complete binary image data as
follows.
[0134] a=250.noteq.255
[0135] b=0
[0136] c=254.noteq.255
[0137] d=2.noteq.0
[0138] Binary data 402 is obtained based on the threshold value
"128" as follows.
[0139] a=255="1"
[0140] b=0="0"
[0141] c=255="1"
[0142] d=0="0"
[0143] Additionally, differential data 403 becomes as follows.
[0144] a=255-250=5
[0145] b=0-0=0
[0146] c=255-254=1
[0147] d=2-0=2
[0148] The restored data 408 shown in FIG. 4B is complete binary
image data since the differential data 402 is quantized after being
subjected to the sub-band transformation. That is, the quantization
is performed so that the LL component of the differential data
becomes 6-bit data, each of the HL and LH components of the
differential data becomes 5-bit data and the HH component of the
differential data becomes 4-bit data. According to such a
quantization method, a fixed length encoding can be achieved with a
reduced amount of encoded information. Thus, the binary image data
can be corrected to complete binary image data by eliminating a
fluctuation generated when the original image is scanned.
Additionally, when the thus quantized data is encoded by an entropy
encoding method, the-quantized data can be compressed at a high
compression rate since each of the sub-band transformation factors
LL, HL, LH and HH is "0". Thus, a compression rate, which is almost
equal to a compression rate when an image is simply binarized, can
be achieved.
[0149] A description will now be given, with reference to FIGS. 5
and 6, of a case in which an image having deterioration is
compressed, the deterioration of the image being peculiar to an
image having a block in which gradation is sharply changed. For the
sake of simplification, it is assumed that a one-dimensional
transformation is used so as to transform original data 501 (x0,
x1)=(96, 191) as follows.
[0150] (1) The original data 501 is binarized based on the
threshold value "128".fwdarw.(0, 255);
[0151] (2) Differential data 502 between the original image data
and one of the binary data "255" and "0" is obtained.
.fwdarw.96-0=96, 255-191=64 Then, the obtained differential data
502 is transformed by the Harr Wavelet transform method.
.fwdarw.L=(96+64)/2=80, H=64-96=-32
[0152] (3) The data L and H are quantized (lower order bits are
deleted).
[0153] Since the low-frequency component L is more important than
the high-frequency component H, the high-frequency component H is
quantized roughly (more number of lower order bits are deleted)
while the low-frequency component L is quantized finely (less
number of lower order bits are deleted). If the low-frequency
component L is not quantized and the high-frequency component H is
quantized by a multiple of "64", the low-frequency component L and
the high-frequency component H become as follows.
[0154] L=80, H=0
[0155] The thus obtained L and H are restored to the differential
data 503 by a reverse sub-band transformation. Then, the decoded
data 504 is restored from the differential data 503. That is, as
shown in FIG. 5, the decoded data 504 is changed from (96, 191) to
(80, 175). That is, the intensities of both pixel values in the
decoded data 504 are decreased from those of the original pixel
values. This phenomenon is generated when a pixel which is rendered
to be the value "255" and a pixel which is rendered to be the value
"0" are present in the same pixel block. This phenomenon is also
generated in a two-dimensional pixel block. Thus, there is a
problem in that there is a considerable difference between an
original image and a restored image.
[0156] In order to eliminate the above-mentioned problem, in the
present invention, when both a pixel rendered to be the value "255"
and a pixel rendered to be the value "0" are present in the same
pixel block, the high-frequency components are quantized finely
(less number of lower order bits are deleted). Referring to FIG. 6,
pixel data 601 (96, 191) is transformed into data 602 including
binary data and differential data (96, 64). The differential data
(96, 64) is transformed by the Harr Wavelet transform method, and
factors L=80 and H=-32 are obtained.
[0157] When the factors are quantized, the high-frequency component
H is quantized finely, and the low-frequency component L is
quantized coarsely. For example, when the high-frequency component
H is quantized by a multiple of 64, and the low-frequency component
L is quantized by a multiple of 32, the sub-band factors becomes as
follows.
[0158] L=64, H=-32
[0159] These factors are subjected to a reverse sub-band
transformation so as to obtain differential data 603. Then, pixel
data 604 is obtained from the thus-obtained differential data 603.
As shown in FIG. 6, the pixel data 604 is changed from the original
data (96, 191) to (80, 207), but the sum of the two pixel values is
not changed. Accordingly, a relative intensity of the pixels is not
changed.
[0160] Generally, in a pixel block having two binary values, an
intensity of an entire pixel block does not change whereas the
intensity is changed when the high-frequency components are
deteriorated. Accordingly, a change in color over an entire image
can be prevented by finely quantizing the high-frequency components
H.
[0161] A description will now be given of a second embodiment of
the present invention. In the second embodiment, the high-frequency
components are not sampled on each individual component basis but a
sampling operation is performed on a combination of the
high-frequency components so as to achieve an efficient sampling
operation. An image block having the HL component representing a
vertical edge and the LH component representing a horizontal edge
being a large value rarely appears in an image. If an image block
has one of the HL and the LH components being a large value and the
other one of the HL and LH components being a small value and also
the HH component being a small value, such an image block
corresponds to either a vertical line or horizontal line in the
image. Thus, such an image block frequently appears in an image.
Additionally, if an image block has the high-frequency components
HL, LH and HH all of which are small values, such an image block
corresponds to an image area having a uniform intensity or an image
area having a gentle change in intensity. Thus, such a block
frequently appears in an image. Accordingly, the image data can be
efficiently quantized by appropriately assigning codes to a
combination of the high-frequency components. This quantization is
referred to as a vector quantization.
[0162] FIG. 7 is a table showing a code assignment to various
combinations of the high-frequency components HL, LH and HH which
frequently appear in an image. Codes "0" through "15" are assigned
to the combinations of the high-frequency components HL, LH and HH.
According to this method, each of the high-frequency components HL
and LH can be compressed into 4-bit data. In the quantizing method,
a difference P between the transformation factor and each value of
the vector quantization is obtained for each individual component,
and a quantization code which minimizes the difference P is
used.
P=.vertline.HL-HLqi.vertline.+.vertline.LH-LHqi.vertline.+.vertline.HH-HHq-
i.vertline.
[0163] Where HLqi, LHqi and HHqi are quantization values
corresponding to a code value i in the quantization table shown in
FIG. 7.
[0164] FIG. 8 is a block diagram of an image processing system
according to the second embodiment of the present invention. FIG. 9
is an illustration for explaining a transformation of image data
performed by the image processing system shown in FIG. 8
[0165] As shown in FIG. 8, the image processing system according to
the second embodiment of the present invention has the same
structure as the image processing system according to the first
embodiment except for a low-frequency component quantizing unit 206
and a high-frequency component quantizing unit 207 being added
between the sub-band transform unit 203 and the entropy encoding
units 204b and 204c.
[0166] The 2.times.2 matrix pixel data extracted and stored in the
buffer unit 201 is transformed into binary image data and
differential data by the binary image differential data producing
unit 202. Then, the binary data is encoded by the entropy encoding
unit 204a by using the entropy encoding method. The encoded data is
stored in the memory 205. The differential data is transformed by
the sub-band transform unit 203 by using the Harr Wavelet
transformation method. The low-frequency component LL of the
transformed data is quantized by the low-frequency component
quantizing unit 206, and then the quantized data is encoded by the
entropy encoding unit 204b. The encoded data is stored in the
memory 205. The high-frequency components HL, LH and HH of the
transformed data are quantized by the high-frequency component
quantizing unit 207, and then the quantized data is encoded by the
entropy encoding unit 204c. The encoded data is stored in the
memory 205.
[0167] In FIG. 9, image data 901 which is the same as that shown in
FIG. 3 is compressed and enlarged.
[0168] a=175
[0169] b=0
[0170] c=175
[0171] d=20
[0172] The binary data 902 is obtained based on the threshold value
"128" as follows.
[0173] a=255="1"
[0174] b=0="0"
[0175] c=255="1"
[0176] d=0="0"
[0177] The differential data 902 becomes as follows.
[0178] a=255-175=80
[0179] b=0-0=0
[0180] c=255-175=70
[0181] d=20-0=0
[0182] The differential data 902 is subjected to a sub-band
transform using the equation (1), and the following data 903 is
obtained as a result.
[0183] LL=45
[0184] HL=70
[0185] LH=-10
[0186] HH=20
[0187] The LL component is subjected to a linear quantization using
quantization representing values which are multiples of "4", and
the following result is obtained.
[0188] LL=44
[0189] As for the high frequency components HL, LH and HH, the
following combination which is closest to the combination (HL, LH,
HH)=(70, -10, 20) is selected from the quantizaiton table shown in
FIG. 7.
(HL, LH, HH)=(64, 0, 0)
[0190] The corresponding code 5 in the quantization table is set to
the vector quantization value H (H=5).
[0191] When an encoding operation is performed, differential data
908 restored based on factors 907 which are (LL, HL, LH, HH)=(44,
64, 0, 0) becomes as follows.
[0192] a=76
[0193] b=12
[0194] c=76
[0195] d=12
[0196] Finally, image data 909 is obtained from the differential
data 908 based on the binary data a=255, b=0, c=255 and d=12 as
follows.
[0197] a=255-76=179
[0198] b=12-0=12
[0199] c=155-76=179
[0200] d=12-0=12
[0201] As mentioned above, according to the second embodiment of
the present invention, the high-frequency components can be
represented by 4-bit data in total since the high-frequency
components are quantized by the vector quantizing method.
Additionally, the LL component becomes 5-bit data by quantizing the
original 7-bit data using a multiple of 4. Further, the binary
image data can be represented by 4-bit data. Thus, the entire
factor corresponding to the 2.times.2 pixel block can be
represented by 13-bit data. Thus, a fixed length encoding method,
which is more efficient than a method which does not use the vector
quantization, can be achieved.
[0202] A description will now be given, with reference to FIGS. 10
and 11, of a third embodiment of the present invention. FIG. 10 is
a block diagram of an image processing system according to the
third embodiment of the present invention. FIG. 11 is an
illustration for explaining a compressing and expanding operation
performed by the image processing system shown in FIG. 10. In FIG.
10, a sub-band transform unit 1202 transforms the image data
corresponding to the pixels a through d received from a 2.times.2
buffer unit 1201 by using the Harr Wavelet transform method so as
to obtain the low-frequency component LL and the high-frequency
components HL, LH and HH. At his time, decimals are omitted. When a
reverse sub-band transform is performed, the original image data
corresponding to the pixels a through d is restored based on the
components LL, HL, LH and HH and the equations (1).
[0203] The LL component takes a value from "0" to "255", and is
represented by 8-bit data. Each of the HL and LH components takes a
value from "-255" to "255", and is represented by 9-bit data. The
HH component takes a value from "-510" to "510", and is represented
by 10-bit data. Thus, the total number of bits is 36. The
importance of each of the sub-band transformation factors LL, HL,
LH and HH is different, and a large part of lower order bits of the
high-frequency components HL, LH and HH can be deleted.
[0204] Accordingly, in this embodiment, a LL component quantizing
unit 1203 quantizes the LL component into a multiple of 4. An HL
component quantizing unit 1204 quantizes the HL component into a
multiple of 16. An LH component quantizing unit 1205 quantizes the
LH component into a multiple of 16. The HH component is rendered to
be "0" that is the HH component is discarded. Specifically, the LL
component is transformed from 8-bit data to 6-bit data by being
divided by 4. Each of the HL and LH components is transformed from
9-bit data to 5-bit data by being divided by 16. The HH component
is transformed from 10-bit data to 0 by being discarded.
Accordingly, the total number of bits of the factors of the
2.times.2 pixel block is reduced from 36 to 16. The quantized
values obtained by the quantizing units are stored in a page memory
1206.
[0205] A description will now be given, with reference to FIG. 11,
of a specific operation of the image processing system shown in
FIG. 10.
[0206] It is assumed that the following image data 1301
corresponding to the pixels a through d shown in FIG. 2 is input to
the sub-band transform unit 1202.
[0207] a=200
[0208] b=202
[0209] c=204
[0210] d=208
[0211] The image data is transformed by the Harr Wavelet transform
method and the following factors 1302 are obtained.
[0212] LL=203
[0213] HL=-3
[0214] LH=-5
[0215] HH=2
[0216] Lower order bits of the factors 1302 are deleted, and the
following quantization data 1303 is obtained.
[0217] LL=50
[0218] HL=0
[0219] LH=0
[0220] HH=0
[0221] The following quantization data 1303 is stored in the page
memory 1206, and then two 0-bits are added so as to obtain the
following factors 1304.
[0222] LL=200
[0223] HL=0
[0224] LH=0
[0225] HH=0
[0226] Thereafter, the factors 1304 are transformed by the reverse
Harr Wavelet transform method so as to restore the following image
data 1305.
[0227] a=200
[0228] b=200
[0229] c=200
[0230] d=200
[0231] Accordingly, despite of the number of bits being greatly
reduced, the restored image data 1305 is almost equal to the
original image data 1301. Additionally, the fixed length encoding
using the sub-band transform can be performed by simple
calculations such as addition and subtraction in the equations (1)
and a bit shift. Additionally, a good image quality can be obtained
by the fixed-length encoding according to the present
embodiment.
[0232] A description will now be given, with reference to FIGS. 12
to 14, of a fourth embodiment of the present invention. FIG. 12 is
a block diagram of an image processing system according to the
present invention. The image processing system according to the
fourth embodiment of the present invention comprises the 2.times.2
buffer unit 1201, the sub-band transform unit 1202 and the page
memory 1206. The image processing unit shown in FIG. 12 further
comprises an area discriminating unit 1403 and a sub-band transform
factor quantizing and encoding unit 1404. The area discriminating
unit 1403 classifies areas of the image into one of an edge area
having a sharp gradation change and a non-edge area other than the
edge area based on the factors HL and LH which are transformed by
the sub-band transform unit 1202. Specifically, the area
discriminating unit 1403 determines that an area to be processed is
an edge area if an absolute value of one of the HL and LH
components is equal to or greater than a threshold value "64". The
area discriminating unit 1403 determines that the area to be
processed is the non-edge area if an absolute value of one of the
HL and LH components is less than the threshold value "64". If the
area to be processed is determined to be the edge area, the area
discriminating unit 1403 supplies a value "1" as a flag value to
the sub-band transform factor quantizing and encoding unit 1404 and
the page memory 1206. If the area to be processed is determined to
be the non-edge area, the area discriminating unit 1403 supplies a
value "0" as a flag value to the sub-band transform factor
quantizing and encoding unit 1404 and the page memory 1206.
[0233] The sub-band transform factor quantizing and encoding unit
1404 quantizes the LL component of the edge area by a multiple of 4
(divide by 4) so as to change the LL component from 8-bit data to
6-bit data. Additionally, the sub-band transform factor quantizing
and encoding unit 1404 quantizes each of the HL and LH components
by a multiple of 64 (divide by 64) so as to change each of the HL
and LH components from 9-bit data to 3-bit data. With respect to
the non-edge area, the sub-band transform factor quantizing and
encoding unit 1404 quantizes the LL component by a multiple of 4
(divide by 4) so as to change the LL component from 8-bit data to
6-bit data. Additionally, the sub-band transform factor quantizing
and encoding unit 1404 quantizes each of the HL and LH components
by rendering an absolute value of each of the HL and LH components
to become one of values 0, 16, 32 and 48 so as to change the HL and
LH components from 9-bit data to 3-bit data. Additionally, the HH
component of either the edge area or the non-edge area is discarded
so as to change the HH component from 10-bit data to 0.
[0234] A description will now be given, with reference to FIGS. 14A
and 14B, of a specific example of an operation of the image
processing system shown in FIG. 12. FIG. 14A shows a case in which
the following image data 1501 which is extracted from a non-edge
area is processed.
[0235] a=200
[0236] b=202
[0237] c=204
[0238] d=208
[0239] The image data 1501 is subjected to the Harr Wavelet
transform, and the following factors 1502 are obtained.
[0240] LL=203
[0241] HL=-3
[0242] LH=-5
[0243] HH=2
[0244] Since absolute values of both the HL and LH components are
less than the threshold value "64", the pixel block is determined
as the non-edge area. Accordingly, the LL component is quantized by
a multiple of 4 and the HL and LH components are divided by 16 and
the HH component is discarded, which results as follows
[0245] LL=50 (6 bits)
[0246] HL=0 (3 bits)
[0247] LH=0 (3 bits)
[0248] HH=0 (0 bit)
[0249] Flag=0 (1 bit)
[0250] Total=13 bits
[0251] The quantized data 1503 is stored in the page memory 1206,
and then 0-bits are added to obtain the following factors 1504. The
number of added 0-bits corresponds to the number of the previously
deleted bits.
[0252] LL=200
[0253] HL=0
[0254] LH=0
[0255] HH=0
[0256] Thereafter, the factors 1504 are subjected to the reverse
Harr Wavelet transform so as to obtain decoded image data 1505 as
follows.
[0257] a=200
[0258] b=200
[0259] c=200
[0260] d=200
[0261] It should be appreciated that the decoded image data is
roughly equal to the original image data 1501.
[0262] FIG. 14B shows a case in which the following image data 1511
that is extracted from an edge area is processed.
[0263] a=20
[0264] b=30
[0265] c=120
[0266] d=150
[0267] The image data 1511 is subjected to the Harr Wavelet
transform, and the following factors 1512 are obtained.
[0268] LL=80
[0269] HL=-20
[0270] LH=-110
[0271] HH=20
[0272] Since the absolute value of the LH component is not less
than the threshold value "64", the pixel block is determined as the
edge area. Accordingly, the LL component is quantized by a multiple
of 4 and the HL and LH components are quantized by a multiple of 64
and the HH component is discarded, which produces results as
follows.
[0273] LL=20 (6 bits)
[0274] HL=0 (3 bits)
[0275] LH=-1 (3 bits)
[0276] HH=0 (0 bit)
[0277] Flag=1 (1 bit)
[0278] Total=13 bits
[0279] The quantized data 1513 is stored in the page memory 1206,
and then 0-bits are added to obtain the following factors 1514. The
number of the added 0-bits corresponds to the number of the
previously deleted bits.
[0280] LL=80
[0281] HL=0
[0282] LH=-64
[0283] HH=0
[0284] Thereafter, the factors 1514 are subjected to the Harr
Wavelet transform so as to obtain decoded image data 1515 as
follows.
[0285] a=24
[0286] b=24
[0287] c=112
[0288] d=112
[0289] It should be appreciated that the decoded image data 1515
still represents an edge area despite of a large number of bits
being deleted.
[0290] In the third embodiment, the HL and LH components are
quantized by a multiple of 16 irrespective of whether the pixel
block to be processed corresponds to the edge area or the non-edge
area. However, in the fourth embodiment, the HL and LH components
are quantized by a multiple of 64 when the absolute values of the
HL and LH components are equal to or greater than the threshold
value "64". Thus, the quantization representative values such as
"96" or "106" are eliminated. Thus, the number of bits of the
quantized data in the fourth embodiment is less than that of the
third embodiment. However, in the edge area in which a gradation
change is sharp, recognizable deterioration in the image is rarely
generated when the values of the high-frequency components are
coarsely sampled. Thus, the information regarding the
high-frequency components can be deleted while the image quality is
maintained at a certain level. Thus, the fixed-length encoding can
be achieved by the above-mentioned separation of an image area.
[0291] A description will now be given, with reference to FIGS. 15
to 18, of a fifth embodiment of the present invention. The fifth
embodiment has the same structure as that of the fourth embodiment
except for the area discriminating unit 1403 and the sub-band
transform factor quantization an encoding unit 1404 being replaced
by an area discriminating unit 1403a and a sub-band transform
factor quantizing and encoding unit 1404a. The area discriminating
area 1403a classifies areas of the image into one of an edge area
having a sharp gradation change and a non-edge area other than the
edge area based on the factors HL and LH which are transformed by
the sub-band transform unit 1202. Specifically, the area
discriminating unit 1403a determines that an area to be processed
is an edge area if an absolute value of one of the HL and LH
components is equal to or greater than a threshold value "16".
Otherwise, the area discriminating unit 1403a determines that the
area to be processed is the non-edge area if an absolute value of
one of the HL and LH components is less than the threshold value
"16". If the area to be processed is determined to be the edge
area, the area discriminating unit 1403a supplies a value "1" as a
flag value to the sub-band transform factor quantizing and encoding
unit 1404a and the page memory 1206. If the area to be processed is
determined to be the non-edge area, the area discriminating unit
1403a supplies a value "0" as a flag value to the sub-band
transform factor quantizing and encoding unit 1404 and the page
memory 1206.
[0292] The sub-band transform factor quantizing and encoding unit
1404a changes a bit assign ratio used by a fixed-length encoding
method in response to the determination as to whether the image
area to be processed is the edge area or the non-edge area. For
example, for the non-edge area, 6 bits are assigned to the LL
component since a gradation is important for visual sense, whereas
3 bits are assigned to each of the HL and LH components. On the
other hand, for the edge-area, 4 bits are assigned to the LL
component since recognition of an edge is important for visual
sense, and 4 bits are assigned to each of the HL and LH
components.
[0293] That is, for the edge-area, the sub-band transform factor
quantizing and encoding unit 1404a quantizes the LL component of
the edge area by a multiple of 16 (divide by 16) so as to change
the LL component from 8-bit data to 4-bit data. Additionally, the
sub-band transform factor quantizing and encoding unit 1404a
quantizes each of the HL and LH components by a multiple of 32
(divide by 32) so as to change each of the HL and LH components
from 9-bit data to 4-bit data. With respect to the non-edge area,
the sub-band transform factor quantizing and encoding unit 1404a
quantizes the LL component by a multiple of 4 (divide by 4) so as
to change the LL component from 8-bit data to 6-bit data.
Additionally, the sub-band transform factor quantizing and encoding
unit 1404a quantizes each of the HL and LH components by a multiple
of 4 (divide by 4) so as to change the HL and LH components from
9-bit data to 3-bit data. Additionally, the HH component of either
the edge area or the non-edge area is discarded so as to change the
HH component from 10-bit data to 0.
[0294] A description will now be given, with reference to FIGS. 18A
and 18B, of a specific example of an operation of the image
processing system according to the present embodiment. FIG. 18A
shows a case in which the following image data 1801 which is
extracted from a non-edge area is processed.
[0295] a=200
[0296] b=202
[0297] c=204
[0298] d=208
[0299] The image data 1801 is subjected to the Harr Wavelet
transform, and the following factors 1802 are obtained.
[0300] LL=203
[0301] HL=-3
[0302] LH=-5
[0303] HH=2
[0304] Since absolute values of both the HL and LH components are
less than the threshold value "16", the pixel block is determined
as the non-edge area. Accordingly, the LL component is quantized by
a multiple of 4, the HL and LH components are quantized by a
multiple of 4 and the HH component is discarded, which produces
results as follows.
[0305] LL=50 (6 bits)
[0306] HL=0 (3 bits)
[0307] LH=0 (3 bits)
[0308] HH=0 (0 bit)
[0309] Flag=0 (1 bit)
[0310] Total=13 bits
[0311] The quantized data 1803 is stored in the page memory 1206,
and then 0-bits are added to obtain the following factors 1804. The
number of added 0-bits corresponds to the number of the previously
deleted bits.
[0312] LL=200
[0313] HL=0
[0314] LH=-4
[0315] HH=0
[0316] Thereafter, the factors 1804 are subjected to the reverse
Harr Wavelet transform so as to obtain decoded image data 1805 as
follows.
[0317] a=198
[0318] b=198
[0319] c=202
[0320] d=202
[0321] FIG. 18B shows a case in which the following image data 1811
which is extracted from an edge area is processed.
[0322] a=20
[0323] b=30
[0324] c=120
[0325] d=150
[0326] The image data 1811 is subjected to the Harr Wavelet
transform, and the following factors 1812 are obtained.
[0327] LL=80
[0328] HL=-20
[0329] LH=-110
[0330] HH=20
[0331] Since the absolute value of the LH component is not less
than the threshold value "16", the pixel block is determined as the
edge area. Accordingly, the LL component is quantized by a multiple
of 4, the HL and LH components are quantized by a multiple of 32
and the HH component is discarded, which results as follows
[0332] LL=5 (4 bits)
[0333] HL=0 (4 bits)
[0334] LH=-3 (4 bits)
[0335] HH=0 (0 bit)
[0336] Flag=1 (1 bit)
[0337] Total=13 bits
[0338] The quantized data 1813 is stored in the page memory 1206,
and then 0-bits are added to obtain the following factors 1814. The
number of the added 0-bits corresponds to the number of the
previously deleted bits.
[0339] LL=80
[0340] HL=0
[0341] LH=-96
[0342] HH=0
[0343] Thereafter, the factors 1514 are subjected to the reverse
Harr Wavelet transform so as to obtain decoded image data 1515 as
follows.
[0344] a=32
[0345] b=32
[0346] c=128
[0347] d=128
[0348] In the third embodiment, the HL and LH components are
quantized by a multiple of 16 irrespective of whether the pixel
block to be processed corresponds to the edge area or the non-edge
area. However, in the fifth embodiment, the factors are encoded by
using different numbers of assigned bits in response to the
determination as to whether the image area corresponds to the edge
area or the non-edge area. That is, the LL component of the
non-edge area in which gradation is important is finely sampled,
whereas the HL and LH components of the edge area in which a change
in intensity is important are finely sampled. Thus, the
fixed-length encoding can be achieved while the feature of an image
is maintained.
[0349] A description will now be given, with reference to FIGS. 19
and 20, of a sixth embodiment of the present invention. FIG. 19 is
a block diagram of an image processing system according to the
sixth embodiment of the present invention. FIG. 20 is an
illustration for explaining a compressing and expanding operation
performed by the image processing system shown in FIG. 19.
[0350] In the present embodiment, pixel values a through d are
subjected to the Harr Wavelet transform by the sub-band transform
unit 1202 as shown in FIG. 12. Then, the low-frequency component LL
is quantized by the low-frequency quantizing unit 206 shown in FIG.
8, and the high-frequency components HL, LH and HH are
vector-quantized by the vector quantizing unit 207 based on the
quantization table shown in FIG. 7.
[0351] Referring to FIG. 20, the following original image data 1101
is input to the sub-band transform unit 1202.
[0352] a=20
[0353] b=30
[0354] c=120
[0355] d=150
[0356] The original image data 1101 is subjected to the Harr
Wavelet transform, and the following factors 1102 are obtained.
[0357] LL=80 (8 bits)
[0358] HL=-20 (9 bits)
[0359] LH=-110 (9 bits)
[0360] HH=20 (10 bits)
[0361] Total 36 bits
[0362] The LL component is represented by 6-bit data as
follows.
[0363] LL=20 (6 bits)
[0364] The HL, LH and HH components (-20, -110, 20) are selected
from the quantization table shown in FIG. 7. That is, the following
values which are closest to the combination of the values (-20,
-110, 20) is selected.
(HL, LH, HH)=(0, -128, 0)
[0365] Then, the corresponding code "12" (4 bits) which represents
the quantization representative vector is set as the quantization
value. Thus, the original image data 1101 is compressed to the data
1103 having 10 bits as the total number of bits.
[0366] In a decoding process, two 0-bits are added to the LL
component (LL=20 (6 bits)). Then, the quantized data 1103 is
decoded to the data 1104 based on the vector quantization value
"12" as follows.
(LL, HL, LH, HH)=(80, 0, -128, 0)
[0367] Thereafter, the data 1104 is subject to the reverse Harr
Wavelet transform, and the following image data 1105 is
obtained.
[0368] a=16
[0369] b=16
[0370] c=144
[0371] d=144
[0372] A description will now be given, with reference to FIGS. 21
to 23, of a seventh embodiment of the present invention. In the
seventh embodiment, the bit assignment of the fifth embodiment and
the vector quantization of the high-frequency components are
combined.
[0373] In the present embodiment, similar to the fifth embodiment,
it is determined that an area to be processed is an edge area if an
absolute value of one of the HL and LH components is equal to or
greater than a threshold value "16". On the other hand, it is
determined that the area to be processed is the non-edge area if an
absolute value of one of the HL and LH components is less than the
threshold value "16". If the area to be processed is determined to
be the edge area, four higher order bits are assigned to the
low-frequency component LL, and four lower order bits are assigned
to the high frequency components HL, LH and HH as shown in FIG.
22A. On the other hand, if the area to be processed is the non-edge
area, six higher order bits are assigned to the low-frequency
component LL and two lower order bits are assigned to the
high-frequency components HL, LH and HH as shown in FIG. 22B.
Additionally, the two lower order bits are used to indicate flag
information as shown in FIG. 22C. That is, the two lower order bits
are set to "00" for the non-edge area, and "00". is not set to the
two lower order bits for the edge area.
[0374] With respect to quantization, as shown in FIG. 22D, the LL
component of the edge area is divided by 16 so as to obtain 4-bit
data, and the high-frequency components are vector-quantized to be
represented by 4-bit data. It should be noted that values 0=0000,
4=0100, 8=1000 and 12=1100 are not used since these values have the
two lower order bits "00" which is reserved for the flag
information. Additionally, the LL component of the non-edge area is
divided by 4 so as to obtain 6-bit data, and only "00" is used for
the high-frequency components.
[0375] FIG. 23A is an illustration for explaining a compressing
operation performed in the seventh embodiment, and FIG. 23B is an
illustration for explaining an enlarging operation performed in the
seventh embodiment.
[0376] It is assumed that the following image data 1301 which is
extracted from the edge area is input.
[0377] a=20
[0378] b=30
[0379] c=120
[0380] d=150
[0381] The image data 1301 is subjected to the Harr Wavelet
transform, and the following factors 1302 are obtained
[0382] LL=80
[0383] HL=-20
[0384] LH=-110
[0385] HH=20
[0386] Since absolute values of both the HL and LH components are
greater than the threshold value "16", the block being processed is
determined as the edge area. Thus, the LL component is divided by
16 and the result is as follows.
LL=20=1010 (4 bits)
[0387] With respect to the high-frequency components, the following
combination which is closest to the HL, LH and HH components (-20,
-110, 20) is selected.
(HL, LH, HH)=(0, -128, 0)
[0388] Additionally, the code "13=0111 (4 bits)" representing the
quantization representative vector is set to the quantization value
1303. Thus, the quantized data 1304 having a total number of bits
being 8 bits is obtained. The quantized data 1304 is stored in the
page memory 1206 as data 1305.
[0389] When encoding is performed, the data 1305 being processes is
read from the page memory 1206 as data 1306. The data 1306 is
determined as the non-edge area since the two lower order bits are
not "00". Thus, the following factors 1308 are obtained.
[0390] LL=80
[0391] HL=0
[0392] LH=-128
[0393] HH=0
[0394] Thereafter, the factors 1308 are subject to the reverse Harr
Wavelet transform, and the following decoded image data 1309 is
obtained.
[0395] a=16
[0396] b=16
[0397] c=144
[0398] d=144
[0399] As mentioned above, according to the seventh embodiment,
since there is no need to assign bits for indicating the flag
information, the image data can be efficiently encoded when the
number of bits used by the fixed length encoding method is
limited.
[0400] A description will now be given, with reference to FIGS.
24A, 24B, 24C and 24D, of an eighth embodiment of the present
invention.
[0401] FIG. 24A is an illustration of 8-bit data corresponding to
the edge area obtained in the above-mentioned seventh embodiment.
The 8-bit data comprises four higher order bits which represent the
LL component and four lower order bits which represent the vector
quantization value of the high-frequency components. Generally,
since a pixel block having a large intensity change rarely appears
in an image, most of the vector quantization values take small
values when the quantization table shown in FIG. 22D is used.
Accordingly, in many cases, each of the two most significant bits
is "0", and each of the two least significant bits is randomly
either "1" or "0".
[0402] FIG. 24B is an illustration of 8-bit data corresponding to
the non-edge area obtained in the above-mentioned seventh
embodiment. The 8-bit data comprises six higher order bits which
represent the LL component and two lower order bits which represent
the flag information. Each of the two lower order bits among the
six higher order bits is randomly either "1" or "0", and each of
the two lower order bits is always "0".
[0403] In the above-mentioned arrangement of bits, the four most
significant bits of each of the 8-bit data corresponding to the
edge area and the non-edge area represent the four most significant
bits of the LL component. These four most significant bits have a
good correlation to each other. Each of the fifth and sixth order
bits counted from the most significant bit corresponding to the
edge area is "0" in most cases. However, each of the fifth and
sixth order bits randomly takes either the value "1". or "0". That
is, the good correlation is lost due to low correlation of the
non-edge area. Additionally, each of the seventh and eighth order
bits counted from the most significant bit has a good correlation
in the non-edge area since it is always "0" for the non-edge area.
However, the seventh and eighth order bits take randomly either "1"
or "0" for the edge area and, thus, correlation is lost due to the
randomness for the edge area.
[0404] Accordingly, in the eighth embodiment, the fifth order bit
and seventh order bit counted from the most significant bit of the
8-bit data corresponding to the non-edge area are exchanged, and
the sixth order bit and eighth order bit counted from the most
significant bit of the 8-bit data corresponding to the non-edge
area are exchanged. According to this rearrangement of the order of
bits, the codes (8-bit data) for the edge area and the non-edge
area can be in a good correlation. This is because the first to
fourth order bits counted from the most significant bit for the
8-bit data corresponding to both the edge-area and the non-edge
area represent the four most significant bits of the LL component;
each of the fifth and sixth order bits counted from the most
significant bit is "0" in most cases for both the edge area and
non-edge area; and each of the seventh and eighth order bits
counted from the most significant bit is randomly either "1" or "0"
for both the edge area and non-edge area. As a result, when the
data in the page memory is stored in another memory for sorting,
the data can be efficiently compressed by the entropy encoding
method.
[0405] A description will now be given, with reference to FIGS. 25
to 30, of a ninth embodiment of the present invention. FIG. 25 is a
block diagram of a printer system which is an image processing
system according to the ninth embodiment of the present
invention.
[0406] The printer system shown in FIG. 25 comprises an image
processing unit 2100, a host unit 2200 such as a personal computer,
a hard disc drive unit (HDD) 2300 as a large capacity memory unit
and a printer engine 2400.
[0407] The image processing unit 2100 comprises an RIP unit 2101, a
frame memory 2102, a block dividing unit 2103, a wavelet transform
unit 2104, a quantizing unit 2105, an encoding unit 2106, a reverse
wavelet transform unit 2107, an edge degree calculating unit 2108
and a gain changing unit 2109.
[0408] A description will now be given of an operation of the
above-mentioned components of the printer system shown in FIG.
25.
[0409] When image data is input from the host unit 2200 to the RIP
unit 2101, the RIP unit 2101 transforms the image data to bit map
data and stores the bit map data in the frame memory 2102. Then,
the bit map data corresponding to a single page is transferred to
the block dividing unit 2103. The block dividing unit 2103 divides
the bit map data into a plurality of block data each having a
predetermined size. The block data is sequentially transferred to
the wavelet transform unit 2104. The wavelet transform unit 2104
transforms the block data to a transform factor, and sends the
transform factor to the quantizing unit 2105 and the edge degree
calculating unit 2108. The edge degree calculating unit 2108
calculates a slope of a rate of change in intensity in the block
data based on the data supplied by the wavelet transform unit 2104.
The result of the calculation is output to the gain changing unit
2109. The gain changing unit 109 changes a gain of the quantizing
unit 2105 based on the intensity slope calculated by the edge
degree calculating unit-2108. The quantizing unit 2105 quantizes
the data supplied by the wavelet transform unit 2104 by a gain
changed by the gain changing unit 2109. The quantized data is
transferred to the encoding unit 2106. The encoding unit 2106 is
provided with a buffer memory having a small capacity. The encoding
unit 2106 performs a data compression (encoding) such as the
QM-coder by using the buffer memory. The compressed data is
sequentially written in the HDD 2300. The compressed data stored in
the HDD 2300 is read upon request, and is supplied to the encoding
unit 2106. Then, the compressed data is expanded by the encoding
unit 2106 by using a reverse encoding method. Thereafter, the
quantizing unit 2105 restores the expanded data by a reverse
quantizing method, and supplies the factor to the reverse wavelet
transform unit 2107. Accordingly, the image data is regenerated by
the reverse wavelet transform unit 2107, and is output as a visible
image by the printer engine 2400.
[0410] The image processing system according to the present
embodiment uses a sub-band encoding method for two-dimensional
image data. Accordingly, as shown in FIG. 26, the wavelet transform
unit 2104 first separates a horizontal direction signal of the
original image into a low-frequency signal and a high-frequency
signal by using a low-pass filter (LPF) 2011 and a high-pass filter
(HPF) 2012. Then, a vertical signal of the original image is
subjected to the same process by low-pass filters 2013 and 2014 and
high-pass filters 2015 and 2016. Thus, the original image data is
separated into four bands, which are, horizontal high-band (HL),
vertical high-band (LH), a diagonal high-band (HH) and a low band
(LL) as shown in FIG. 27.
[0411] When the band separation is performed, the original image is
divided into a plurality of 2.times.2 pixel matrix blocks as shown
in FIG. 27. That is, each pixel block comprises two horizontally
arranged pixels and two vertically arranged pixels. First, the
pixels in the pixel block are subjected to a (2, 2) transformation
in the horizontal direction based on the following equation (11).
In this operation, an LPF output s(n) and an HPF output d(n) are
obtained. Thereafter, each of the outputs is subjected to a (2, 2)
transformation in a vertical direction so that the transform factor
shown in FIG. 27 is obtained. The horizontal high-band HL
represents a high-frequency component of the original image in the
horizontal direction. The vertical high-band LH represents a
high-frequency component in a vertical direction. The diagonal
high-band HH represents a high-frequency component in a diagonal
direction. The low band LL represents a low-frequency
component.
[0412] It should be noted that the original image may be divided
into 4.times.4 matrix pixel blocks. In such a case, each of the
components (HL, LH, HH, LL) includes four components.
[0413] The transform factor is restored to image data by a (2, 2)
reverse transform based on the following equation (2) after being
subjected to a quantizing process, an entropy encoding process and
a reverse quantizing process described later.
"(2, 2) transform"
LPF: s(n)=.left brkt-bot.{X(2n)+X(2n+1)}/2.right brkt-bot.
HPF: d(n)=X(2n)-X(2n+1) (11)
"(2, 2) reverse transform"
X(2n)=s(n)+.left brkt-bot.{d(n)+1}/2.right brkt-bot.
X(2n+1)=s(n)-.left brkt-bot.d(n)/2.right brkt-bot. (12)
[0414] Generally, a probability density function of high-frequency
factors is approximated by the Laplacian distribution having a mean
value of 0. Thus, the quantizing unit 2105 performs a non-linear
quantization with respect to high-frequency factors so as to
minimize average quantization noise power.
[0415] FIG. 28 is a graph showing a quantizing characteristic of
the quantizing unit 2105. Actually, the transform factor takes
either a plus value or a minus value. Since the plus and minus
values are symmetric, only a plus side is shown in the figure. In
the example shown in FIG. 28, 511 values between -255 and 255 are
quantized into 9 values, which are, -219, -125, -70, -30, 0, 30,
70, 125, 219. These quantization representative values are
compressed by n entropy encoding such as the Huffman encoding.
[0416] The quantizing unit 2105 determines one of ranges into which
an input value falls, the ranges being defined by plurality of
threshold values. Then, the quantizing unit 105 outputs one of the
quantization representative values corresponding to the one of the
ranges into which the input value falls.
[0417] If the gain of the quantizing unit 2105 is set to 1.0, the
quantization representative value lies between the two adjacent
threshold values which defines the range. For example, in an
example shown in FIG. 29B, the quantization representative value 70
is a value between the threshold values 50 and 98. However, if the
gain is not 1.0, this condition is not established. That is, in an
example shown in FIG. 29A in which the gain is set to 2.0 which is
greater than 1.0, the quantization representative value 70 is a
value within a range between 25 and 49. On the other hand, in an
example shown in FIG. 29C in which the gain is set to 0.5 which is
smaller than 1.0, the quantization representative value 70 is
assigned to a range between 101 and 196.
[0418] If the gain of the quantizing unit 2105 is greater than 1.0,
an output of the quantizing unit 2105 is always greater than an
input thereof. On the contrary, if the gain of the quantizing unit
2105 is smaller than 1.0, the output of the quantizing unit 2105 is
always smaller than the input thereof.
[0419] Sharpness of change in gradation of a block is proportional
to a magnitude of an absolute value of the high-frequency factor.
Thus, if the gain of the quantization 2105 with respect to the
high-frequency factor is set to a value grater than 1.0, a slope of
change in intensity of the image restored by a reverse transform
becomes steeper. On the contrary, if the gain is set to a value
less than 1.0, the slope becomes gentler.
[0420] If the gradation change in the block is sharp, the absolute
value of the high-frequency factor is increased.
[0421] The high-frequency factor of an area having a sharp
gradation change, such as a character image area or a line image
area, takes a relatively large value as shown in FIG. 30A. However,
the high-frequency factor of an area, in which the gradation change
is gentle such as a photograph image, takes a relatively small
value.
[0422] Accordingly, the edge degree calculating unit 2108
calculates a difference d between the largest component and the
second largest component among the three components LH, HL and HH
of the high-frequency factor. The calculated difference d is
determined as an edge degree.
[0423] If the 4.times.4 pixel matrix is used, each of the three
components of the high-frequency factor includes four components.
Thus, one of the four components having the maximum absolute value
is selected as a representative of the corresponding one of the
three components. Then, a difference d between the largest
representative value and the second largest representative value is
calculated.
[0424] As mentioned above, in an area such as a character image
area or a line image area, a differential filtering process is
performed so as to increase an intensity slope of a contour of the
image. On the other hand, in an area such as a photograph image in
which an intensity slope is gentle, a smoothing filtering process
is performed.
[0425] Conventionally, the filtering process for adjusting an image
quality and a quantizing process are performed in different
processes. However, in the present embodiment, the filtering
process is performed in the data compressing process. This is
achieved by the gain changing unit 2109 changing the gain of the
quantizing unit 2105 in response o the edge degree calculated by
the edge degree calculating unit 2108. Thus, the process time is
reduced and a manufacturing cost of the system can be reduced. The
gain of the quantizing unit 2105 is changed by changing the
threshold values thereof. New threshold values are obtained by
dividing the threshold values at the gain of 1.0 by a desired
gain.
[0426] Accordingly, if the desired gain is previously defined by a
function of the edge degree, the new threshold values can be
obtained sequentially in an order of high-frequency
factor.fwdarw.edge degree.fwdarw.gain.fwdarw.new threshold value.
Thus, the gain of the quantizing unit 2105 can be changed to the
desired gain by the new threshold values.
[0427] When the gain of the quantizing unit 2105 is changed in
response to a type of image area, there is no need to announce the
quantization table used when the encoding is performed to the
decoding side since the quantization representative value is fixed,
that is, not changed. In the conventional technique such as
disclosed in the aforementioned patent document, information with
respect to the used quantization table must be stored together with
the image data in the compressed data. However, in the present
embodiment, such information regarding the quantization table is
not necessarily stored as compressed data and, thus, the
compression rate is increased.
[0428] If the information is omitted at a part which is not
sensitive to a visual sense of human beings, the compression rate
can be increased without changing an image quality. For example, a
small intensity change adjacent to an area having a sharp intensity
slope is hardly recognized. This effect is known as a mask effect.
According to the mask effect, when a difference between edge
degrees of adjacent blocks exceeds a predetermined value, the
intensity change of the block having the smaller edge degree is
hardly recognized.
[0429] Accordingly, when a difference between edge degrees of
adjacent blocks exceeds a predetermined value, the edge degree
calculating unit 2108 changes the smaller edge degree to a further
smaller values As a result, the gain of the quantizing unit 2105 is
changed to a smaller value by the gain changing unit 2109 and,
thereby, dispersion of the quantized value is decreased which
result in an increase in the compression rate.
[0430] Additionally, in an area in which mesh point images or line
images are randomly and densely populated, there may be no problem
in a visual sense even when the smoothing process is performed. In
many cases, a plurality of components of the high-frequency factor
of such an area may be large values as shown in FIG. 30C.
[0431] In such a case, the edge degree calculating unit 2108 does
set the largest component to the edge degree but set a difference
between the largest component and the second largest component to
the edge degree. Thereby, the edge degree is changed to a smaller
value. As a result, the gain of the quantizing unit 2105 is changed
to a smaller value by the gain changing unit 2109 and, thus,
dispersion of the quantized value is decreased which results in an
increase in a compression rate.
[0432] A description will now be given, with reference to FIG. 31,
of a tenth embodiment of the present invention. FIG. 31 is a block
diagram of a printer system which is an image processing system
according to the tenth embodiment of the present invention. In FIG.
31, parts that are the same as the parts shown in FIG. 25 are give
the same reference numerals.
[0433] The printer system shown in FIG. 31 comprises an image
processing unit 3100, a host unit 2200 such as a personal computer,
a hard disc drive unit (HDD) 2300 as a large capacity memory unit
and a printer engine 2400.
[0434] The image processing unit 3100 comprises an RIP unit 2101, a
frame memory 2102, a block dividing unit 2103, a wavelet transform
unit 2104, an image area discriminating unit 3105, a quantizing
unit 3106, an encoding unit 2106 and a reverse wavelet transform
unit 2107.
[0435] A description will now be given of an operation of the
above-mentioned components of the printer system shown in FIG.
31.
[0436] When image data is input from the host unit 2200 to the RIP
unit 2101, the RIP unit 2101 transforms the image data to bit map
data and stores the bit map data in the frame memory 2102. Then,
the bit map data corresponding to a single page is transferred to
the block dividing unit 2103. The block dividing unit 2103 divides
the bit map data into a plurality of block data each having a
predetermined size. The block data is sequentially transferred to
the wavelet transform unit 2104. The wavelet transform unit 2104
transforms the block data to a transform factor, and sends the
transform factor to the image area discriminating unit 3105 and the
quantizing unit 3106. The image area discriminating unit 3105
determines whether each block corresponds to an edge area or a
non-edge area based on the data from the wavelet transform unit
2104. The quantizing unit 3106 quantizes the data from the wavelet
transform unit 2104 based on a result of a determination of the
image area discriminating unit 3105. The quantized data is
transferred to the encoding unit 2106. The encoding unit 2106 is
provided with a buffer memory having a small capacity. The encoding
unit 2106 performs a data compression (encoding) such as the
QM-coder by using the buffer memory. The compressed data is
sequentially written in the HDD 2300. The compressed data stored in
the HDD 2300 is read upon request, and is supplied to the encoding
unit 2106. Then, the compressed data is expanded by the encoding
unit 2106 by using a reverse encoding method. Thereafter, the
quantizing unit 3106 restores the expanded data by a reverse
quantizing method, and supplies the factor to the reverse wavelet
transform unit 2107. Accordingly, the image data is regenerated by
the reverse wavelet transform unit 2107, and is output as a visible
image by the printer engine 2400.
[0437] The image processing system according to the present
embodiment uses a sub-band encoding method for two-dimensional
image data. An operation of the sub-band encoding method used in
this embodiment is the same as that of the ninth embodiment as
shown in FIG. 26, and a description thereof will be omitted.
[0438] When the band separation is performed, the original image is
divided into a plurality of 2.times.2 pixel matrix blocks (refer to
FIG. 27). That is, each pixel block comprises two horizontally
arranged pixels and two vertically arranged pixels. First, the
pixels in the pixel block are subjected to a (2, 6) transformation
in the horizontal direction based on the following equations (13).
In this operation, an LPF output s(n) and an HPF output d(n) are
obtained. Thereafter, each of the outputs is subjected to a (2, 2)
transformation in a vertical direction based on the above-mentioned
equation (11) so that the transform factor shown in FIG. 27 is
obtained. The horizontal high-band HL represents a high-frequency
component of the original image in the horizontal direction. The
vertical high-band LH represents a high-frequency component in a
vertical direction. The diagonal high-band HH represents a
high-frequency component in a diagonal direction. The low band LL
represents a low-frequency component.
"(2, 6) transform"
LPF: s(n)=.left brkt-bot.{X(2n)+X(2n+1)}/2.right brkt-bot.
HPF: d(n)=X(2n)-X(2n+1)+.left brkt-bot.{-S(n-1)+s(n+1)+2}/4.right
brkt-bot. (13)
"(2, 6) reverse transform
X(2n)=s(n)+.left brkt-bot.{d(n)-.left
brkt-bot.[-S(n-1)+s(n+1)+2]/4.right brkt-bot.}/2.right
brkt-bot.
X(2n+1)=s(n)-.left brkt-bot.{d(n)-.left
brkt-bot.[-S(n-1)+s(n+1)+2]/4.righ- t brkt-bot.}/2.right brkt-bot.
(14)
[0439] The image area discriminating unit 3105 determines whether
each block corresponds to an edge area or a non-edge area based on
the spatial gradation change in the block. If the gradation change
is sharp, an absolute value of the high-frequency component is
large. That is, if the absolute value of the high-frequency
component is greater than a predetermined value, is can be
determined as an edge area. Otherwise, it can be determined as a
non-edge area. For example, blocks satisfying the following
relationship (15) are determined as edge area blocks, and blocks
other than the edge area blocks are determined as non-edge area
blocks.
31<.vertline.HL.vertline. or 31<.vertline.LH.vertline. or
31<.vertline.HH.vertline. (15)
[0440] Generally, a probability density function of high-frequency
factors is approximated by the Laplacian distribution having a mean
value of 0. Thus, the quantizing unit 3106 performs a non-linear
quantization with respect to high-frequency factors so as to
minimize average quantization noise power.
[0441] FIG. 32A shows an example of a two-dimensional vector
quantization using the components HL and LH, the vector
quantization being represented by 7 values (3 bits). FIG. 32B shows
an example of a two-dimensional vector quantization using the
components HL and LH, the vector quantization being represented by
15 values (4 bits).
[0442] Since the high-frequency component HH has less importance in
a visual sense, the high-frequency component is discarded in the
present embodiment. Additionally, the probability density function
of the low-frequency component LL fluctuates for images, and there
is no correlation. Thus, the probability density function of the
low-frequency component LL is regarded as a uniform distribution,
and is subjected to a linear quantization. If the component LL is 8
bits and a 3-bit quantization is performed, five least significant
bits are deleted. If a 4-bit quantization is performed, four least
significant bits are deleted.
[0443] The quantizing unit 3106 changes a ratio of numbers of bits
assigned to the high-frequency component and the low-frequency
component while the number of bits assigned to a single block is
maintained to be the same. That is, a greater number of bits are
deleted from the high-frequency component of the edge area, whereas
a smaller number of bits are deleted from the high-frequency
component of the edge area. However, the number of bits of the
high-frequency component of the edge area is equal to the number of
bits of the low-frequency component of the non-edge area.
[0444] FIG. 33 is an illustration for explaining an example of a
bit arrangement for each area. In the example shown in FIG. 33, 8
bits are always assigned to a single block. For the edge area, 4
bits are assigned to the high-frequency component, 3 bits to the
low-frequency component, and 1 bit to area information. For the
non-edge area, 3 bits are assigned to the high-frequency component,
4 bits to the low-frequency component, and 1 bit to area
information.
[0445] As mentioned above, the encoding unit 2106 produces data for
compensating errors so as to produce an image having higher image
by complementing the data represented by a quantization factor
having a fixed length for a single block. For example, a difference
between the original image and the restored image is previously
obtained, the restored image being obtained from the quantizing
factor having a fixed length by being subjected to a reverse
transform using the equations (12) and (14). Then, the quantization
factor and data (error data) corresponding to the above-mentioned
difference are encoded by an entropy encoding method.
[0446] The above-mentioned quantization factor and the error data
are compressed (encoded) by a variable length reverse encoding
method such as the QM-coder. When an image is rotated by a printer,
data structure is preferably a fixed length. Thus, in such a case,
a rotation of the image is performed prior to the entropy
encoding.
[0447] As mentioned above, the compressed data read from the HDD
2300 is reverse encoded (decoded) by the encoding unit 2106, and is
reverse quantized by the quantizing unit 3106 so as to return to
the transform factor. The transform factor is subjected to a
reverse transform by the reverse wavelet transform unit 2107 so as
to restore the image data. At this time, both the quantization
factor and the error data are decoded so as to restore the
transform factor. Then, the transform factor is subjected to
reverse transform and, thereby, the original image can be almost
completely restored. Additionally, an image having an image quality
at a certain level can be restored by reverse transforming the
quantized factor having a fixed length, that is, both the
high-frequency component and the low-frequency component.
[0448] Additionally, when only the high-frequency component of the
quantized factors with a fixed length is reverse transformed for
the edge area, and when only the low-frequency component of the
quantized factor with a fixed length is reverse transformed for the
non-edge area, image quality is considerably deteriorated, but a
feature of the original image can be still maintained. Such a
restored image can be used for a trial printing.
[0449] Further, if the decoding process of the image is not
performed for all blocks but for every other block as shown in FIG.
34, image quality and resolution may be deteriorated, but the
restored image can still provide a certain feature of the original
image. Such a restored image can be used as an icon on a display
unit.
[0450] The present invention is not limited to the specifically
disclosed embodiments, and variations and modifications may be made
without departing from the scope of the present invention.
[0451] The present application is based on Japanese priority
applications No. 9-118207 filed on May 8, 1997, No.9-156006 filed
on May 29, 1997 and No.9-156007 file on May 29, 1998, the entire
contents of which are hereby incorporated by reference.
* * * * *