U.S. patent application number 13/757525 was filed with the patent office on 2014-08-07 for video quality analyzer.
The applicant listed for this patent is Michael Shinsky, Victor Steinberg. Invention is credited to Michael Shinsky, Victor Steinberg.
Application Number | 20140218543 13/757525 |
Document ID | / |
Family ID | 51135682 |
Filed Date | 2014-08-07 |
United States Patent
Application |
20140218543 |
Kind Code |
A1 |
Steinberg; Victor ; et
al. |
August 7, 2014 |
VIDEO QUALITY ANALYZER
Abstract
Methods, systems and software are disclosed for automated
Measurement of Video Quality parameters. The system includes a
static Test Pattern provided either in form of a Test Pattern File,
converted via a standard playout device (test source) into analog
or digital test signal and supplied to the input of a System Under
Test, or in form of a Reflectance Chart installed before the
front-end device of the System Under Test, such as TV camera. The
system also includes a video capture device connected to the
back-end device of the System Under Test, e.g. to the output of
system decoder/player. A Video Quality Analyzer processes the
captured video data and generates a detailed Analysis Report.
Inventors: |
Steinberg; Victor; (Santa
Clara, CA) ; Shinsky; Michael; (Santa Clara,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Steinberg; Victor
Shinsky; Michael |
Santa Clara
Santa Clara |
CA
CA |
US
US |
|
|
Family ID: |
51135682 |
Appl. No.: |
13/757525 |
Filed: |
February 1, 2013 |
Current U.S.
Class: |
348/188 |
Current CPC
Class: |
H04N 17/002 20130101;
H04N 17/004 20130101 |
Class at
Publication: |
348/188 |
International
Class: |
H04N 17/00 20060101
H04N017/00 |
Claims
1. A video monitoring system to perform automated Measurement of
Video Quality parameters, comprising: a static test pattern
provided as a test pattern file rendered by a device under test or
a reflectance chart captured by a camera under test, wherein the
test pattern file is identical to the reflectance chart and
includes a multitude of video test components combined in one test
pattern including horizontal test bands forming a multi-row matrix,
each test band containing test pattern components specific for a
particular sub-set of video quality parameters; a video capture
device coupled to the device under test or camera under test to
generate video data for analysis; and a video quality analyzer
processor processing the video data into an analysis report,
wherein color co-ordinates of captured image test components are
compared with known reference values.
2. The system of claim 1, wherein the test pattern contains video
components for (1) aural and visual estimation, (2) on-line or
off-line instrumental analysis, and (3) fully automated on-line or
off-line analysis.
3. The system of claim 1, wherein the test pattern includes a
multitude of video test components combined in one test pattern
including horizontal test bands forming a multi-row matrix, each
test band containing test pattern components specific for a
particular sub-set of video quality parameters.
4. The system of claim 1, wherein the test pattern includes test
bands consisting of Color Bars, Inverted Grayscale, Direct
Grayscale, Frequency Bursts, and Multi-Pulse.
5. The system of claim 1, wherein the test pattern components
include several circular Geometry Reference Markers, and optional
enhancement components on a flat color background.
6. The system of claim 5, wherein the Geometry Reference Markers
within the Test Pattern comprise circles, filled with two contrast
colors providing for reliable differentiation of said Markers from
the rest of test pattern and accurate positioning of circle centers
locations within a captured video frame.
7. The system of claim 5, wherein the Geometry Reference Markers
are located at four corners of a rectangle derived by downscaling
of a Test Pattern outer boundary with a fixed scaling factor.
8. The system of claim 1, wherein XY co-ordinates of test pattern
components within a video frame are re-mapped for measurement
purposes from original positions to actual positions using scaling
and offset coefficients based on previously measured XY positions
of the Reference Markers.
9. The system of claim 1, comprising one or more Color Bars Band
shown in two versions differing in color saturation: (1) full
saturation version for Test Pattern File and (2) reduced saturation
version for Reflectance Chart.
10. The system of claim 1, comprising one or more Grayscale Bands
with one of: a Black Shallow Ramp Insert ("Near-Blacks"), and a
White Shallow Ramp Insert ("Near-Whites"), purposed for YRGB Range
Black Level Overload and YRGB Range White Level Overload
measurements.
11. The system of claim 1, wherein the video quality analyzer
starts with a detection of Reference Markers relative positions
within a captured video frame and finishes with a creation of
Report File(s) including results of measurement steps and Summary
Table showing Results Values in line with the user-defined Target
Values.
12. The system of claim 1, wherein the number of video quality
analysis steps depends on a detection of valid Reference Markers
relative positions.
13. The system of claim 1, wherein after successful detection of
valid Reference Markers, the video quality analysis includes Image
Geometry Measurements, Pulse Response Measurements, YUV/YRGB Levels
measurements, Y Gamma and YRGB Range Overload Measurements,
Frequency Response Measurements and Noise Measurements.
14. The system of claim 1, wherein after unsuccessful Reference
Markers detection, the video quality analysis comprises Noise
Measurements only.
15. The system of claim 1, wherein the video quality analyzer
performs a YUV/YRGB Levels analysis including a comparison of
actual measured levels with a set of pre-calculated Reference
Levels, whilst these Reference Levels in turn depend on automatic
Test Chart Type detection (differing in full saturation vs. reduced
saturation) and manual or automatic Color Scheme selection.
16. The system of claim 1, wherein an automatic Color Scheme is
selected based on a comparison of actual measured RGB levels with
several sets of Reference Levels, each set representing Color Bars
values for one Color Scheme to select a Scheme providing for the
smallest maximal error (minimum distance in the RGB color
space).
17. The system of claim 1, wherein the video quality analyzer
performs automatic Test Chart Type selection based on a comparison
of actual measured RGB Color Bars levels with two sets of
pre-calculated Reference Levels and the results of Color Saturation
measurement based on a comparison of relative gain of Colored Pulse
components in the Multi-Pulse Band--Y gain vs. UV gain.
18. The system of claim 1, wherein the test pattern contains video
components for (1) aural and visual estimation, (2) on-line or
off-line instrumental analysis, and (3) fully automated on-line or
off-line analysis, wherein the test pattern includes a multitude of
video test components combined in one test pattern including
horizontal test bands forming a multi-row matrix, each test band
containing test pattern components specific for a particular
sub-set of video quality parameters, wherein the test pattern
includes test bands consisting of Color Bars, Inverted Grayscale,
Direct Grayscale, Frequency Bursts, and Multi-Pulse, wherein the
test pattern components include Geometry Reference Markers, and
optional enhancement components on a flat color background, and
wherein the Geometry Reference Markers within the Test Pattern
comprise circles, filled with two contrast colors providing for
reliable differentiation of the said Markers from the rest of test
pattern and accurate positioning of circle centers locations within
a captured video frame.
19. The system of claim 1, wherein after successful detection of
valid Reference Markers, the video quality analyzer performs Image
Geometry Measurements, Pulse Response Measurements, YUV/YRGB Levels
measurements, Y Gamma and YRGB Range Overload Measurements,
Frequency Response Measurements and Noise Measurements and wherein,
after unsuccessful Reference Markers detection, the video quality
analyzer performs Noise Measurements only.
20. The system of claim 1, wherein the video quality analyzer
performs a YUV/YRGB Levels analysis including a comparison of
actual measured levels with a set of pre-calculated Reference
Levels, whilst these Reference Levels in turn depend on automatic
Test Chart Type detection (differing in full saturation vs. reduced
saturation) and manual or automatic Color Scheme selection and
wherein an automatic Color Scheme is selected based on a comparison
of actual measured RGB levels with several sets of Reference
Levels, each set representing Color Bars values for one Color
Scheme to select a Scheme providing for the smallest maximal error
(minimum distance in the RGB color space).
Description
[0001] This application is a continuation of Ser. No. 13/225,476,
filed Sep. 4, 2011, the content of which is incorporated by
reference.
BACKGROUND
[0002] With the introduction of advanced digital delivery systems
for audio and video, there is an increased awareness of the
relationship between subjective (perceived) quality and objective
(measured) quality of video images presented to the observer's eye.
Video quality is a characteristic of a video passed through a video
transmission/processing system, a formal or informal measure of
perceived video degradation (typically, compared to the original
video). Video processing systems may introduce some noticeable
amounts of distortion or artifacts in the video signal, so video
quality evaluation is an important problem.
[0003] Currently there are many tools for analyzing video quality
utilizing the Full Reference Methods (FR) such as dual-stimulus
methodology based on calculation of differences between original
and processed video data and subsequent transformation of these
differences in accordance with predetermined metrics.
[0004] Typically, objective methods are often classified based on
the availability of the original video signal, which is considered
to be of high quality (generally not compressed). These metrics are
usually used when the video coding method is known. PSNR (Peak
Signal-to-Noise Ratio) is the most widely used objective video
quality metric. However, PSNR values do not perfectly correlate
with a perceived visual quality due to non-linear behavior of human
visual system. The PSNR calculation on the pre-selected set of live
clips is very long and tedious job, so in fact it is executed only
during acceptance test of some large-scale systems. In other words,
this methodology is not suitable for fast measurement of large
quantity of different video processors and/or processing
modes/profiles. More sophisticated metrics require even more
calculations, thus they are even less suitable for fast objective
measurements.
[0005] Moreover, PSNR compression artifacts metering implies that
both A and B picture have same resolution, horizontal and vertical
positions, video levels and (very important)--same frequency
response, i.e. both pictures are perfectly aligned in space and
time. Only under these conditions PSNR reading correlates well with
subjective quality estimates. In modern content delivery systems
such conditions are very seldom satisfied.
[0006] A second approach is represented by well established
techniques of measuring objective video processing parameters on
some artificial matrix test pattern. This approach captures video
data and subsequently analyzes the captured video data. However,
automatic video analyzers in this approach suffer from lack of
flexibility: they are limited to a short list of video image
resolutions and signal formats--any image size/position/resolution
deviation from perfect match results in a failure of the analysis
process. Additionally, analysis of pre-captured data files is not
supported. With application to the analysis of video cameras
performance, analyzers of this kind provide mainly waveform monitor
functionality, i.e. only manual controls, thus excluding any
automated analysis.
[0007] A third approach is represented, for example, by IE-Analyzer
made by Image Engineering, Gmbh in Germany. This automated
hardware/software tool is suitable for accurate and detailed camera
performance analysis, but requires a nearly perfect setup of
lighting conditions and camera's pan/zoom/tilt controls.
IE-Analyzer can work with pre-captured files, but positioning of
dotted lines delimiting the ROI (Region Of Interest) should be done
manually. Moreover, for each reported parameter a different
reflectance test chart or test pattern transparency is required, so
the complete measurement process takes a long time, and nearly
perfect studio conditions and highly skilled technical personnel
are pre-requisites.
SUMMARY
[0008] In a first aspect, methods, systems and software are
disclosed for automated Measurement of Video Quality parameters.
The system includes a static Test Pattern provided either in form
of a Test Pattern File, converted via a standard playout device
(test source) into analog or digital test signal and supplied to
the input of a System Under Test, or in form of a Reflectance Chart
installed before the front-end device of the System Under Test,
such as TV camera. The system also includes a video capture device
connected to the back-end device of the System Under Test, e.g. to
the output of system decoder/player. A Video Quality Analyzer
processes the captured video data and generates a detailed Analysis
Report.
[0009] In a second aspect, a video monitoring system to perform
automated Measurement of Video Quality parameters includes a static
test pattern provided as a test pattern file or a reflectance
chart, the test pattern file rendered by a device under test, the
reflectance chart capture by a camera under test, wherein the
device under test or the camera under test generates video data for
analysis; and a video quality analyzer processor processes the
video data into detailed analysis report.
[0010] Implementations of the above aspects may include one or more
of the following. The Test Pattern contains video components
equally suitable for (1) aural and visual estimation, (2) for
on-line or off-line instrumental analysis, and (3) for fully
automated on-line or off-line analysis. The Test Pattern components
include several horizontal Test Bands, forming multi-row matrix,
each band containing test pattern components specific for the
particular sub-set of video quality parameters, such as video
levels, frequency response, pulse response, etc., thus providing
for a multitude of video test components combined in one test
pattern, e.g. the said Test Pattern includes Test Bands consisting
of (1) Color Bars, (2) Inverted Grayscale, (3) Direct Grayscale,
(4) Frequency Bursts, (5) Multi-Pulse. The Test Pattern components
also include special Geometry Reference Markers, and some more
optional enhancement components on a flat color background, e.g.
50% Gray, such as Vertical Resolution Wedges and/or Radial Mires
and/or Timing Reference dynamic components, e.g. clock dial or
current video frame number display. The Geometry Reference Markers
within the Test Pattern are implemented as several (typically four)
small circles, filled with two contrast colors, e.g. White and
Blue, thus providing for reliable differentiation of the said
Markers from the rest of Test Pattern and accurate positioning of
said circles centers locations within the captured video frame. The
Geometry Reference Markers are located at four corners of the
rectangle derived by the downscaling of the Test Pattern outer
boundary with some known fixed scaling factor, e.g. 0.75. The XY
co-ordinates of all Test Pattern components within is video frame
are re-mapped for measurement purposes from their original (ideal)
positions to their actual positions using the scaling and offset
coefficients based on previously measured XY positions of Reference
Markers. One or more Color Bars Band can be shown in two versions
differing in color saturation: (1) full saturation version for Test
Pattern File, i.e. for signal/data processors testing, and (2)
reduced saturation version for Reflectance Chart, i.e. for video
cameras testing. The Grayscale Bands can include optional Black
Shallow Ramp Insert ("Near-Blacks") and/or White Shallow Ramp
Insert ("Near-Whites"), purposed for more accurate YRGB Range Black
Level Overload and YRGB Range White Level Overload measurements.
The video quality analysis starts with the detection of the
Reference Markers relative positions within the captured video
frame (also used as a prove of Test Pattern and Test Setup
validity) and finishes with the creation of the Report File(s)
including the results of all measurement steps and Summary Table
showing the Results Values in line with the user-defined Target
Values. The number of video quality analysis steps in the said
multi-step process depends on the detection of the valid Reference
Markers relative positions, i.e. on Test Pattern and Test Setup
validity. In case of successful detection of valid Reference
Markers the video quality analysis include Image Geometry
Measurements, Pulse Response Measurements, YUV/YRGB Levels
measurements, Y Gamma and YRGB Range Overload Measurements,
Frequency Response Measurements and Noise Measurements; but in case
of unsuccessful Reference Markers detection the analysis process
collapses to Noise Measurements only. A YUV/YRGB Levels analysis
include the comparison of actual measured levels with a set of
pre-calculated Reference Levels, whilst these Reference Levels in
turn depend on automatic Test Chart Type detection (differing in
full saturation vs. reduced saturation) and manual or automatic
Color Scheme selection--two most important Color Schemes are
"0-255" scheme, used mainly in computer graphics applications, and
"16-235" scheme, commonly used in video applications. An automatic
Color Scheme selection is based on the comparison of actual
measured RGB levels with several sets of Reference Levels, each set
representing Color Bars values for one Color Scheme; result is the
selection of the Scheme providing for the smallest maximal error
(minimum distance in the RGB color space). An automatic Test Chart
Type selection can based on the comparison of actual measured RGB
Color Bars levels with two sets of pre-calculated Reference Levels
and the results of Color Saturation measurement based on the
comparison of relative gain of Colored Pulse components in the
Multi-Pulse Band--Y gain vs. UV gain.
[0011] Advantages of the preferred embodiments may include one or
more of the following. The system uses EXACTLY THE SAME test
pattern for cameras and video processors alike. This is more
convenient than other systems that use EITHER video signal test
matrix, suitable ONLY for video data processors, OR reflectance
charts, suitable ONLY for cameras. These two types of test patterns
traditionally used by other systems for two types of applications
have no similarity at all. The system uses only ONE test pattern
for a variety of opto-electronic systems, such as teleconference
system. This universality allows users to insert and capture test
data at any point in the signal processing chain--from camera lens
to the very last decoder.
[0012] Other advantages of the preferred embodiments may include
one or more of the following. The system accurately characterizes
the most important objective parameters of video processing quality
such as:
[0013] picture geometry described in simplified form as picture
position and size
[0014] video levels traditionally expressed in picture brightness,
contrast, saturation and RGB values
[0015] video image uniformity usually described in terms of
horizontal and vertical "shading"
[0016] picture sharpness traditionally represented by pulse and
frequency response values
[0017] analog and digital noise artifacts traditionally represented
by SNR values
[0018] Objective measurement of the parameters listed above allows
practical objective judgment on picture quality or more precisely
"loss of quality in video processing workflow". For example, the
proposed system can be used for a variety of applications to find
in advance video image distortions associated with particular
profile of video camera, video format conversion device and/or
video compression codec. The system allows drastic improvement of
speed, sophistication and completeness of automated video quality
analysis. The system can create resolution-agnostic video quality
metrics and a testing methodology for objective measurements of
offline or online video processing path without any referral to
particular live video content, but covering all practically used
steps of this content processing--from the camera lens to the
destination side video display input. The system is applicable for
modern multi-format teleconference and content delivery
environment.
[0019] Any consumer or professional system or device that has the
ability to process video images or video data in order to deliver
and/or display video and/or other multi-media content can use the
objective measurement system. The system can also be beneficial for
benchmarking purposes, e.g. for comparison of different cameras or
compression codecs or comparison of different encoding profiles of
the particular encoder. The system is especially useful where the
data processing services are utilizing file-based environment for
the preparation and delivery of video content. The system provides
for fast and accurate analysis of all listed parameters. The
software reliably works within the wide range of video image
conditions in terms of image size and position, relatively big
geometry errors, lighting non-uniformities and in presence of
relatively high embedded noise.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] This system will now be described by way of example with
reference to the accompanying drawings in which:
[0021] FIG. 1 shows variants of analysis workflow.
[0022] FIG. 2 illustrates an exemplary test pattern
composition.
[0023] FIG. 3 shows an exemplary Software Workflow Diagram for a
Video quality analyzer.
[0024] FIG. 4 shows an exemplary Test Result Summary Table for
1920.times.1080 image resolution.
[0025] FIG. 5 shows an exemplary Test Result Summary Table for
1280.times.720 image resolution.
[0026] FIG. 6 shows an exemplary Geometry Test Result for
Reflectance Chart.
[0027] FIG. 7 shows exemplary Video Level Test Results.
[0028] FIG. 8 shows exemplary details of Near-White Test Pattern
used at Levels Test stage.
[0029] FIG. 9 shows exemplary Frequency Response Test Results.
[0030] FIG. 10 shows exemplary Noise Analysis Test Results.
DESCRIPTION
[0031] The following description of the present system is done by
the way of non-exclusive example of how the Video quality analyzer
would work in an environment where video content is distributed
through a video data delivery service such as a videoconferencing
system.
[0032] One embodiment of the analyzer is software that runs on
hardware or components readily available on the market. In the
preferred embodiment, the present system consists of a standard
off-the-shelf video capture device, e.g. Unigraf capture card, and
software executable running under standard OS, e.g. Microsoft
Windows. The system can determine the video quality of digital SD
or HD TV and IPTV data processing cases, in particular--video
cameras, compression codecs, scalers, TV sets, STBs, PCs, or
portable devices.
[0033] The system performs automated Measurement of Video Quality
parameters by a static Test Pattern provided either in form of a
Test Pattern File, converted via a standard playout device (test
source) into analog or digital test signal and supplied to the
input of System Under Test, or in form of a Reflectance Chart
installed before the front-end device of the System Under Test,
such as TV camera. The test pattern is recorded as a data file by a
video capture device connected to the back-end device of the System
Under Test; e.g. to the output of system decoder/player. The test
pattern is then analyzed by a video quality analyzer that in turn
generates a detailed video quality Analysis Report.
[0034] Referring initially to FIG. 1, a reflectance chart 90 is
captured by a camera 92 whose output is directed connected to a
computer (USB or FireWire) or through a capture card 110.
Alternatively, a test file 100 can be played by a reference player
102 and provided to a video processor 106. The test file 100 can
also be played by a video player 104. The output of video player
104 or video processor 106 can be captured by the capture card 110.
The test file 100 can also be encoded by a video encoder 108, and
decoded by a reference decoder 112 if the quality of the
encoder/decoder is being tested. The output of the capture card 110
or the decoder 112 is a video file 114 that can be analyzed by a
video quality analyzer 120 which generates report 130 that contains
diagnostics data and data describing the quality of the video. The
result is an Objective Picture Quality Metering System with
practical application to software, hardware or hybrid devices. The
system's measurement results contribute to the improvement of
perceived quality of static or dynamic digital pictures.
[0035] One purpose of the video quality analyzer is to measure
captured video files from any HD or SD source. With the Reflectance
Chart 90, the system measures video cameras, but through a video
player, processors and codecs the system can measure the overall
performance of complex and sophisticated video data transmission
chains.
[0036] FIG. 2 illustrates an exemplary composition of a Test
Pattern matrix. In this embodiment, a static matrix test pattern
provides for automated measurement algorithms of all relevant video
parameters. This test pattern could be also combined with live
video content to provide objective video quality reference points
along full video distribution chain--from content origination,
through content re-purposing and distribution to final content
consumption at the consumer display. Preferably, the same test
pattern, both in form of optical reflectance chart and in form of
video signal source, is used for consistent testing of full chain
from camera lens to the display screen.
[0037] Turning to FIG. 2, each of five Bands from #1 to #5 is
dedicated to a particular sub-set of video quality parameters. Band
#0 contains several optional visual components, which are not
related to automatic analysis. The test patterns also include
Geometry Reference Markers 10, which provide for features such
as:
1. Geometry checks, such as test chart scaling (zoom), XY offset
(position); in case of Reflectance Chart they also serve to measure
tilt and keystone parameters 2. Test Pattern Validation: if
Reference Markers are not present (not detected) analysis process
collapses to Noise Measurement only 3. All other measurements are
using scaling/positioning coefficients calculated from the detected
Reference Markers positions within the video frame.
[0038] The test pattern also includes a component 20 that provides
for sharpness/spatial shading determination.
[0039] FIG. 3 shows an exemplary process to determine video
quality. The first step 302 is Test Case configuration. At this
stage User selects assumed YUV file format and optionally the
assumed YRGB Range selection. This does not require any significant
changes in the data processing algorithm, but may drastically
change the presentation and interpretation of the analysis
results.
[0040] Next, step 304 consists of data source selection: either
live data from the capture card 306 via the driver 308, or
pre-captured video data stored in the file 310. In both cases video
data can be presented either in YUV (UYVY) format or in RGB format,
among others. In one embodiment, the selection is stored in form of
YUV/RGB Flag, used in all further calculations.
[0041] The result of the acquisition step 304 is large array of
video data 312, which can be single video frame or small group of
video frames, e.g. eight consecutive frames; this array should be
processed during the subsequent steps. The size of this array must
be large enough to accommodate the data. For example, at
1920.times.1080 resolution the required YUV data array size in
bytes is 1920.times.1080.times.2.times.8=approximately 33 MB of RAM
in one embodiment.
[0042] An optional Viewer and Waveform Scope module 314 allows user
to preview incoming images and YUV/RGB waveforms of any line or
averaged group of input video lines. The scope feature is useful in
finding out the reasons of automated analysis failure, e.g. it may
be caused by the incoming video data timing errors.
[0043] At step 316, in one embodiment, acquired data from all
available video frames are first averaged to reduce harmful effect
of embedded noise. The test pattern image is then split into four
quadrants; each quadrant is searched at step 316 for the presence
and position of Reference Markers 10 (FIG. 2).
[0044] At step 318, geometry test parameters, such as H & V
position offset, H & V scaling coefficients, effective chart
size (which can be smaller or bigger than video frame size), image
tilt, keystone distortion, are put together and presented in a
Geometry Test Report using predetermined commonly accepted units,
e.g. in pixels and/or percents of image height. These parameters
can be mathematically calculated based on differences between ideal
and measured positions of Reference Markers, among others.
[0045] If all four Reference Markers are found in approximately
correct positions, then a Test Pattern Validation Flag is
activated. This flag is used in the Geometry Test Report and also
serves to enable several further stages of automated analysis.
[0046] Acceptable marker positions cover wide range of scaling
coefficients--from 110% down to 45% in one embodiment. However, the
range of permitted offsets, tilts and keystone values should be
rather small. For example, if chart image tilt exceeds 10 degrees,
the rectangular arrangement of color patches within the Test
Pattern Bands is significantly deteriorated. In such case the
Validation Flag should be deactivated. Significant H or V offset
also may cause complete loss of some test pattern components, so
large offset values should be avoided. Thus, linear scaling (zoom)
is permitted within reasonable limits, but other geometry
transformations should be restricted.
[0047] All further steps rely on steps 316 and 318 in terms of
re-mapped positions of all measurable components within the Bands
#1, 2, 3, 4 and 5. For example, to find the co-ordinates of
Frequency Burst labeled "1" and located on the Band #4 left side,
its original (default) position should be re-mapped proportionally
to the measured offsets of Reference Markers--as illustrated on
FIG. 6.
[0048] If valid Test Matrix Pattern is not detected, then the whole
analysis process collapses to Noise Test only--following the
workflow control step 320. As described below, noise measurement
does not rely on Reference Markers, so noise can be measured on any
static image, such as full screen color bars or just flat full
field color, e.g. gray field, among others. All results of step 318
are summarized in the Geometry Test Report.
[0049] At step 322 Band #5 (Pulses and Bars) is analyzed, resulting
in K-factor value, measured on "white needle" pulse, and Y vs. UV
Gain (Saturation), measured on soft green pulse.
[0050] The Y_vs_UV_Gain value is important. First, it describes
general image quality deterioration--color saturation loss or
excessive boost. Second, together with the color bars levels
measurement results, it provides for automatic switching between
two modes of operation of the video quality analyzer: "optical"
reflectance chart mode and "electric" test pattern mode. This
switch is created and applied later--at step 324. All results of
step 322 are summarized in Pulse Response Test Report.
[0051] Step 324 consists of preliminary setting of modes of
operation and finding the important general parameters, such as
luminance signal dynamic range on Band #2 (Inverted Grayscale) and
Band #3 (Grayscale), prior to detailed levels analysis applied at
next step 326.
[0052] For greater robustness, Band #2 is mirrored and luminance
values of two bands are averaged, thus minimizing harmful effects
of non-uniform lighting--e.g. if lighting level linearly drops from
left to right, then half-sum of left and right white patches levels
is exactly equal to the white level in the middle portion of the
picture.
[0053] One of the step 324 goals is distinguishing between two
possible Color Schemes: 0-255 scheme, used mainly in computer
graphics applications, and 16-235 scheme, commonly used in video
applications.
[0054] The manual or automatic selection of Color Scheme is
important in one embodiment because it affects the assumed nominal
values of all colors in all bands. The selection of wrong Color
Scheme may jeopardize all color analysis results. The comparison of
actual color bars saturation, measured at step 324, with the
Y_vs_UV_Gain value, measured at the previous step 322, allows
distinguishing between "optical" reflectance chart mode and
"electric" test pattern mode.
[0055] In optical mode color bars saturation is about 6 dB lower
than Y_vs_UV_Gain value; in "electric" mode they should be
approximately equal. This mode switching is needed to select
appropriate reference YUV/RGB color bars values used to calculate
color errors table.
[0056] At step 324 the Y channel "candidate" levels on black and
white patches are tested against the decision thresholds set
half-way between possible nominal values. The Default Scheme is
16-235 ("Video"). If average Y value on black patch is below
0.5*(0+16) and measured Y value on white patch is above
0.5*(255+235), then the Color Scheme 0-255 ("Computer Graphic") is
selected.
[0057] At step 326 Bands #1 (Color Bars), #2 (Inverted Grayscale)
and #3 (Grayscale) are split into several rectangular areas
(patches); each patch contains only one color; examples are Yellow
patch within the Color Bars or 100% White patch of Inverted
Grayscale.
[0058] Video data within the central portion of each patch are
averaged for further suppression of noise and other artefacts. This
results in measured YUV and RGB values for all patches.
[0059] At step 326 the YUV/RGB Flag, set at step 304, is used to
control the direction of color space conversion--either derivation
of RGB values from YUV values (if acquired data are in YUV format)
or vice versa derivation of YUV values from RGB values (if acquired
data are in RGB format). YUV and RGB values of all patches are
further processed to calculate standard colorimetric parameters,
such as Black and White levels, Luminance Gamma, Dynamic Color
Balance Errors, YUV and RGB values of Color Bars, etc. This
includes application of well-known standard color space conversion
coefficients and formulae. For faster finding of erroneous color
values in the presented result tables, special highlight flags can
be created, marking the colors with maximal absolute errors
(maximal RGB space distance from the correct nominal values).
[0060] Near-White and Near-Black inserts within the Bands #2 and #3
require special processing. Unlike other color patches, these
components of test pattern contain linear gradients, so called
shallow ramps.
[0061] YRGB range overloading, e.g. caused by excessive opening of
camera's iris or by "black level crash" of video processor, can be
detected in form in clipping of these shallow ramps.
[0062] The size of clipped area is directly proportional to the
overload strength (percentage of lost dynamic range). Count of
clipped pixels, divided by the total number of pixels in this test
pattern component, represents the percentage of detected overload.
All results of step 326 are summarized in Levels Test Report.
[0063] At step 328 central portions of all frequency bursts within
Band #4 are measured. This creates six pairs of arrays containing
peak and trough values. Differences between peaks and troughs are
averaged, thus finding out the average contrast of each burst. The
contrast values are then referenced to interpolated contrast of
dark gray and light gray patches on both sides of the Band.
[0064] Relative contrast values represent individual bursts
positions along the vertical axis of Frequency Response Plot shown
on FIG. 9.
[0065] Horizontal axis positions on the Frequency Response Plot,
i.e. actual frequencies of the captured bursts, are calculated by
scaling original "pristine" burst frequencies in accordance with
the H scaling coefficient measured at step 318. All results of step
328 are summarized in Frequency Response Test Report.
[0066] Step 330 (noise analysis) includes several stages of spatial
and temporal filtering. The goal of this filtering is the
separation of random noise YUV values from static YUV values of the
test pattern itself. Important feature of this filtering is the
preservation of noise horizontal spectrum shape.
[0067] Because any horizontal filtering is undesirable, noise
separation process consists of vertical-temporal high-pass
filtering. The first stage is temporal filtering, achieved by
deduction of the central frame YUV values from the average YUV
values across eight adjacent video frames. The second stage is
vertical filtering. Many modern video processors involve video line
averaging; typical vertical aperture size of such processor is from
two to five video lines. This may produce a noise of specific
type--highly correlated in vertical dimension. Accurate measurement
of such noise requires vertical filters with the aperture size much
larger than the incoming noise vertical correlation interval.
[0068] This is implemented at step 330 by adding together the
powers (energy) of YUV differences taken across eight video lines.
Eight TV lines is big enough vertical distance, allowing to
overcome the abovementioned problem of vertically correlated video
noise handling.
[0069] Filtered out noise, separate values for Y, U, V, R, G, and B
channels, is then processed by standard statistical formulae,
resulting in standard deviations, histograms and Y noise horizontal
spectrum plots--with and without weighting filters.
[0070] The separated noise values are presented as a viewable image
with "boosted" contrast. This noise image together with the
horizontal spectrum plot allows advanced user to distinguish truly
random noise from periodic interferences, such as cross-talks or
digital clock pick-ups. The results of step 330 are summarized in
Noise Test Report.
[0071] At every step for user convenience all partial test result
reports are presented as plots and tables on several separate pages
(windows), i.e. Geometry Page, Levels Page, among others.
[0072] At step 332 the most important Test Results are compared
with the user-defined target values and presented in three
formats:
1. On-screen Results Summary Table 334
[0073] 2. Detailed printable Report 336, e.g. PDF file 3.
Machine-readable Report 338, e.g. Excel spreadsheet file.
[0074] FIGS. 4-10 show exemplary test results.
[0075] FIG. 4 shows an example of a Test Results Summary Table for
1920.times.1080 image resolution. The Summary Table shows the
measured video quality parameters and corresponding target values
(user-defined tolerances). If the measured result is within the
target range, then this row of the table shows green tick (pass
mark) in the Pass/Fail column. If the measured result is outside of
the target range, then green tick is replaced by a red cross
(failure mark). The scoring of these pass/fail marks provides for
fully automated (unattended) analysis mode. For example, in the
strictest variant, appearance of just one red cross in any row
means that system or device does not pass the test. A thumbnail
picture at the bottom of the Summary Page serves mainly for quick
visual estimate of general test conditions. For example,
significant Reflectance Chart tilt or lighting non-uniformity may
invalidate all test results.
[0076] FIG. 5 shows an example of Test Results Summary Table for
1280.times.720 image resolution. The main difference against FIG. 4
is the size of the thumbnail picture; this display allows quick
visual check of actual video data resolution.
[0077] FIG. 6 shows one example of Geometry Test Results for
Reflectance Chart. In this example, the inner corners of four green
squares indicate calculated positions of the four corners of Test
Chart. Their positions are calculated by extrapolation of the
measured positions of four blue-white Reference Markers. Despite
the fact that upper-left and bottom-left corners are not visible
their calculated positions are contributing to the final
results.
[0078] Turning now to FIG. 7, an example of Video Levels Test
Results is shown. The page contains many partial parameters;
together they give comprehensive presentation of Y, R, G, and B
gradations rendition and inter-channel misbalances. Black Level and
White Level are presented in % of the selected nominal YRGB range
and also in D18 bit levels. Luminance Gamma is calculated by best
fitting method on 9 of 11 staircase porches; two lowest porches are
ignored to minimize noise and glare related effects. RGB Dynamic
Balance Error is a maximum of R-G, B-G and R-B magnitudes of all 11
staircase porches. Black Balance Error and White Balance Error are
calculated similarly, but only the lowest (Black) and the highest
(White) porches are used. Black Crash and White Crash (Y Range
Overload) are measured by finding the clipping level of shallow
ramps in the central area of the Test Pattern. The bottom half of
the page is occupied by Color Bars Table. It contains YUV and RGB
levels of the test pattern measured on Band #1. The Table also
shows (in Gray) the reference values of 100/0/75/0 Color Bars
corresponding to the selected Nominal Range (16-235 or 0-255). The
right half of each cell shows calculated Color Bar Errors, i.e.
differences between measured and reference values. Video data can
come as YUV or as RGB. Values within the "Captured Data" part of
the Table are YUV or RGB data, averaged and rounded to 8 bit values
without any mapping or scaling. Values within the "Derived Values"
part of the Table are results of application of standard Color
Space Conversion Matrix to the input data; these results are also
rounded to 8 bit and compared with the corresponding 8 bit
reference values.
[0079] Referring now to FIG. 8, exemplary details of Near-White
Test Pattern used at Levels Test stage are shown. In this example
all three shallow ramps of R, G and B channels are not
distorted--the ramp waveforms are linear and not clipped.
[0080] FIG. 9 shows another example of Frequency Response Test
Results. This page shows the measured averaged peak-to-peak
amplitudes of six frequency bursts and display of averaged
luminance waveform of multi-burst band (Band 4). The burst
amplitudes are expressed in dB with respect to nominal
(undistorted) value. This band of test pattern includes special
reference bars with levels exactly matching the nominal burst
amplitude. The measurement algorithm checks these bars first, and
automatically compensates for any non-standard Black Level and
White Level conditions, including Levels Tilt. This allows the
frequency response measurement to be always accurate and correct,
independent of any lighting, setup or gain errors in Y channel.
Frequencies are shown above the response values in two formats:
original (pristine chart) values are shown in gray, and actual
scaled values in black. In the example shown they differ only
slightly because the camera zoom (98%) is very close to 100%.
Scaled frequency values are also used for plotting the response
curve at the bottom of the page. On the frequency response plot the
measured values are shown in blue, target limits are shown in
brown.
[0081] FIG. 10 shows yet another example of Noise Analysis Test
Results. The most important noise parameter is RMS noise level of Y
channel displayed in the upper left corner of the page using three
types of units:
[0082] a) % of Nominal White
[0083] b) D18 bit levels (in brackets)
[0084] c) Equivalent mV of analog Y signal (also in brackets)
[0085] Other important noise parameters present on this display
are:
[0086] a) Y channel SNR, calculated in three variants: unfiltered,
band-limited and weighted.
[0087] The Y RMS value (shown above the Y SNR) directly correlates
with unfiltered Y SNR.
[0088] b) UV SNR, derived from band-limited unweighted sum of
scaled U noise and V noise
[0089] c) R, G, B and "Dark B" SNR values, derived from Y and UV
SNRs
[0090] A histogram display in the upper right corner allows
differentiation between truly random Gaussian (i.e. unprocessed)
noise and "cored" noise signal typically produced by noise
reducers. If Y and G histogram plots are close to ideal Gaussian
curve shown in gray, then the effect of noise reduction is rather
small.
[0091] In the example shown the difference between two curves is
very large, which indicates that camera processor applied very deep
noise reduction reducing the relative probabilities of low noise
magnitudes vs. high magnitudes.
[0092] Bottom part of the page contains Y Noise Spectral Density
plots in dB/MHz for unlimited and weighted noise spectra and also
Noise Image with boosted contrast. These two displays allow to see
the effect of device under test frequency response and also to
distinguish random noise from the contributions by regular
textures, e.g. from those caused by RF interference or digital
clock pick-up.
[0093] Various modifications and alterations of the invention will
become apparent to those skilled in the art without departing from
the spirit and scope of the invention, which is defined by the
accompanying claims. It should be noted that steps recited in any
method claims below do not necessarily need to be performed in the
order that they are recited. Those of ordinary skill in the art
will recognize variations in performing the steps from the order in
which they are recited. In addition, the lack of mention or
discussion of a feature, step, or component provides the basis for
claims where the absent feature or component is excluded by way of
a proviso or similar claim language.
[0094] While various embodiments of the present invention have been
described above, it should be understood that they have been
presented by way of example only, and not of limitation. Likewise,
the various diagrams may depict an example architectural or other
configuration for the invention, which is done to aid in
understanding the features and functionality that may be included
in the invention. The invention is not restricted to the
illustrated example architectures or configurations, but the
desired features may be implemented using a variety of alternative
architectures and configurations. Indeed, it will be apparent to
one of skill in the art how alternative functional, logical or
physical partitioning and configurations may be implemented to
implement the desired features of the present invention. Also, a
multitude of different constituent module names other than those
depicted herein may be applied to the various partitions.
Additionally, with regard to flow diagrams, operational
descriptions and method claims, the order in which the steps are
presented herein shall not mandate that various embodiments be
implemented to perform the recited functionality in the same order
unless the context dictates otherwise.
[0095] Although the invention is described above in terms of
various exemplary embodiments and implementations, it should be
understood that the various features, aspects and functionality
described in one or more of the individual embodiments are not
limited in their applicability to the particular embodiment with
which they are described, but instead may be applied, alone or in
various combinations, to one or more of the other embodiments of
the invention, whether or not such embodiments are described and
whether or not such features are presented as being a part of a
described embodiment. Thus the breadth and scope of the present
invention should not be limited by any of the above-described
exemplary embodiments.
[0096] Terms and phrases used in this document, and variations
thereof, unless otherwise expressly stated, should be construed as
open ended as opposed to limiting. As examples of the foregoing:
the term "including" should be read as meaning "including, without
limitation" or the like; the term "example" is used to provide
exemplary instances of the item in discussion, not an exhaustive or
limiting list thereof; the terms "a" or "an" should be read as
meaning "at least one," "one or more" or the like; and adjectives
such as "conventional," "traditional," "normal," "standard,"
"known" and terms of similar meaning should not be construed as
limiting the item described to a given time period or to an item
available as of a given time, but instead should be read to
encompass conventional, traditional, normal, or standard
technologies that may be available or known now or at any time in
the future. Likewise, where this document refers to technologies
that would be apparent or known to one of ordinary skill in the
art, such technologies encompass those apparent or known to the
skilled artisan now or at any time in the future.
[0097] A group of items linked with the conjunction "and" should
not be read as requiring that each and every one of those items be
present in the grouping, but rather should be read as "and/or"
unless expressly stated otherwise. Similarly, a group of items
linked with the conjunction "or" should not be read as requiring
mutual exclusivity among that group, but rather should also be read
as "and/or" unless expressly stated otherwise. Furthermore,
although items, elements or components of the invention may be
described or claimed in the singular, the plural is contemplated to
be within the scope thereof unless limitation to the singular is
explicitly stated.
[0098] The presence of broadening words and phrases such as "one or
more," "at least," "but not limited to" or other like phrases in
some instances shall not be read to mean that the narrower case is
intended or required in instances where such broadening phrases may
be absent. The use of the term "module" does not imply that the
components or functionality described or claimed as part of the
module are all configured in a common package. Indeed, any or all
of the various components of a module, whether control logic or
other components, may be combined in a single package or separately
maintained and may further be distributed across multiple
locations.
[0099] Additionally, the various embodiments set forth herein are
described in terms of exemplary block diagrams, flow charts and
other illustrations. As will become apparent to one of ordinary
skill in the art after reading this document, the illustrated
embodiments and their various alternatives may be implemented
without confinement to the illustrated examples. For example, block
diagrams and their accompanying description should not be construed
as mandating a particular architecture or configuration.
[0100] The previous description of the disclosed embodiments is
provided to enable any person skilled in the art to make or use the
present invention. Various modifications to these embodiments will
be readily apparent to those skilled in the art, and the generic
principles defined herein may be applied to other embodiments
without departing from the spirit or scope of the invention. Thus,
the present invention is not intended to be limited to the
embodiments shown herein but is to be accorded the widest scope
consistent with the principles and novel features disclosed
herein.
* * * * *