an automated image prescreening tool for a printer qualification process by † du-yong ng and ‡...
TRANSCRIPT
An automated image prescreening tool for a printer qualification process
by
†Du-Yong Ng and ‡Jan P. Allebach
† Lexmark International Inc.
‡ School of Electrical and Computer Engineering, Purdue University
Synopsis
Anatomy of a formatter-based EP laser printer
Overview of a printer qualification process
Motivation
Examples of artifacts
Image fidelity metrics – prior work
Prescreening tool
Experiment
Results
Conclusion
Anatomy of a formatter-based EP laser printer
A standalone network device
Print Engine
Formatter
Control Panel
Overview of a printer qualification process
Phase I The formatter (hardware + firmware) and the print engine are
developed in parallel.
The hardware portion of the formatter is not ready.
The firmware of formatter is tested using a simulator .
Test image (PDL)
Newly developedfirmware + simulator
Firmware of an earlierproduct + simulator
Master Current
Digital outputsmatch qualitatively?
Overview of a printer qualification process (cont.)
Phase II Preproduction print engines are either scarce or not available yet.
Preproduction formatter hardware is available.
The formatter (firmware + hardware) is tested with a print engine emulator.
Test image (PDL)
Newly developed formatter + print engine emulator
Formatter of an earlierproduct + print engine emulator
Digital outputsmatch qualitatively?
Master Current
Overview of a printer qualification process (cont.)
Phase III Preproduction printers (formatter + print engine) are available.
Test image (PDL)
Newly developed printerEarlier printer model
Hardcopiesmatch qualitatively?
Master Current
Overview of a printer qualification process - summary
Simulator without actual formatter/ emulator with actual formatter
Test image(PDL)
Softcopy current image
Comparison masters
Softcopy
Formatter/firmware
development teams
Debug
Error flagged
Actual formatter Hardcopy current imagePrint engine
HardcopyDebug
Motivations Image screening (comparison of master-current image pairs)
is mostly performed by trained observers.
is needed for thousands of softcopy and hardcopy image pairs throughout the printer development process.
is very labor intensive.
is often performed only on a fraction of test suites before the product is rolled out due to
» a relatively short development time.» a large number of test images in the test suites.
Our goal is to develop a automated tool to reduce the workload of trained
observers and increase the volume of tests the by screening out softcopy image pairs with
» highly objectionable errors (failed).» visually insignificant errors (passed).
Trained observers only need to focus on image pairs which
could not be screened out by the tool (further evaluation)
Examples of master-current image pair
Master Current
Different halftone algorithms
Missing pixels
Master Current
Examples of master-current image pair (cont.)
Different in character size
Master
Current
Sporadic difference
CurrentMaster
Image fidelity metrics – prior work First category
Examples» Peak-signal-to-noise ratio (PSNR), root mean square error, and ∆Ea*b*
Pros» They are fast and easy to compute, and produce a single number.
Cons» Averaging effect destroys local information and spatial interaction of
pixels is ignored.
Second category Examples
» Wu’s color image fidelity assessor (HVS model) and structural similarity image metric (first and second order statistics the luminance channel of a local window)
Pros» Spatial processing model is included.
Cons» The algorithm is computational expensive and does not produce a
single number (Wu’s color fidelity assessor) » SSIM produces a single number but suffers from the averaging effect.
Prescreening tool - requirements
needs to work reasonably fast.
classifies the master-current pairs into one of the categories:
‘passed’, ‘failed’, and ‘further evaluation required’.
must adapt automatically as the spatial resolution of the images
changes in dpi.
has to be capable of processing any image content.
needs to handle both halftone and continuous-tone images.
will process full color, indexed color, grayscale, and bilevel
images consistently.
will detect and ignore small spatial shifts in content and
differences in orientation
Master image Current image
PASSEDFAILED
The prescreening tool
FURTHER EVALUATION
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Preprocessing
Compute 2D error map
Cluster error map
Compute error metric
Thresholding
Prescreening tool - preprocessing
Determine shift in content
Master Current
• Determine image type
• Perform quick diff
• Determine resolution
• Correct orientation
• Correct spatial shift
Other processing
Con
ten
t si
ze
Imag
e si
ze
Check dimensionRotated CWRotated CCW
Master Current Current
Check orientation
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Prescreening tool – compute and cluster 2D error map (illustration)
Master M i
j
i
j
Current C
i
j
Binary Error Map
j
iClustered Error Map
Clu
stering
kth cluster
(k+1)th cluster
(k+2)th cluster
Prescreening tool – compute error metric (contrast sensitivity function CSF component)
Master i
j
i
j
Currentcorrespond to the lth pixel of the kth cluster
Mkl
Ckl
Average filtered pixel value for the kth cluster
Master :
Current:
Error for the kth cluster:
CSF error component for the image pair:
MM
kllk
k N 1
CC
kllk
k N 1
To CIE L*a*b*Mk
Ck
CMkk
CSF
kbaE
**
k
CSF
kbaktot
CSFba EN
NE ****
1
Prescreening tool – compute error metric (contrast sensitivity function CSF component)
(cont.)
Prescreening tool – compute error metric (visual acuity filter VAF component)
Master i
j
i
j
Currentcorrespond to the lth pixel of the kth cluster
Mkl
Ckl
Average filtered pixel value for the kth cluster
Master :
Current:
Error for the kth cluster:
VAF error component for the image pair:
MM
kllk
k N 1
CC
kllk
k N 1
To CIE L*a*b*Mk
Ck
CMkk
VAF
kbaE
**
k
VAF
kbaktot
VAFba EN
NE ****
1
Prescreening tool – compute error metric (visual acuity filter VAF component)
(cont.)
Prescreening tool – compute error metric (overall error)
Combined error
Error metric value for the image pair
maperror thein pixels of # total1 where
**
totf
NVAFCSFba
NN
Ef
Ea*b*{ }CSF+VAF
= ΔEa*b*{ }CSF⎡
⎣⎤⎦
N p
+ ΔEa*b*{ }VAF⎡
⎣⎤⎦
N p
{ }
1
N p
where N p = 1+ 2 ⋅tanh(max( ΔEa*b*{ }CSF
, ΔEa*b*{ }VAF
))
Experiment There are 147 image pairs.
Test image pairs are of 300, 600 and 1200 dpi. binary, grayscale, color bitmap and full color.
Six expert observers classify the image pairs into 3 categories Acceptable (passed). Need further evaluation. Objectionable (failed).
These image pairs are processed with our prescreening tool.
The peak signal to noise ratio (PSNR) metric and structural similarity image metric (SSIM) are also computed for each preprocessed (to ensure fair comparison) image pair.
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Results – error metric 22
A zero error metric value indicates a perfect match.
Decision thresholds exist to pass and fail image pairs.
The prescreening tool is able to screen out as many as 35% of the image pairs tested.
Results - PSNR A larger PSNR value (PSNR = ∞ for a perfect match) indicates a closer
match.
Only decision thresholds to pass image pairs exist.
The PSNR metric is able to screen out only as many as 4% of the image pairs.
Results - SSIM 24
A larger SSIM value (SSIM = 1 for a perfect match) indicates a closer match.
Only decision thresholds to fail image pairs exist.
The SSIM metric is able to screen out only as many as 3.4% of the image pairs.
Conclusion
We have successfully developed an automated prescreening tool
along with an image fidelity metric for a printer qualification
process.
This tool works for a wide range of image types, content, and
image resolutions.
It requires no training and it is able to reduce the workload of
expert observers by a substantial amount.
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