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Post-processing of JPEG image using MLP
Fall 2003 ECE539 Final Project Report Data Fok
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Overview Introduction
Approach
Experiments & Results
Conclusion
Demo
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Introduction
Increase demand on graphic usage Graphics: large file size JPEG compression blocking artifact Unpopularity of JPEG 2000 Removal of JPEG artifact
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Approach
Multi Layer Perception 15 inputs (5 x 3)
5 R,G,B gradients of the neighbor pixels close to the block border
6 outputs (2 x 3) 2 R,G,B different of the original image and
the compressed image on the pixels next to the block border
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Approach – cont.
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Approach – cont.
First order polynomial fit
Use the 4 pixels closest to the block border to estimate the value on the 2 pixels next to the border
Use as a control experiment
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Approach – cont.
Image quality evaluate by Human eyes Peak signal to noise ratio (PSNR)
MSEPSNR
255log10 10
2
,
2),(ˆ),(
MN
yxIyxI
MSE yx
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Experiment & Result
Optimal MLP structure after testing
Structure: 15-5-6
Learning rate = 0.01
Momentum = 0.7
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Experiment & Result – cont. Expt #1: grayscale image
train and test with the same image
JPEG (0.14 bpp)PSNR = 41.2044 (dB)
MLP postprocessedPSNR = 40.2514 (dB)
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Experiment & Result – cont. Expt #2: color image
train and test with the same image
JPEG (0.18 bpp)PSNR = 38.2464 (dB)
MLP postprocessedPSNR = 37.9718 (dB)
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Experiment & Result – cont. Expt #3: grayscale image
train with a high bpp image, test with a low bpp image
JPEG (0.085 bpp)PSNR = 39.5696 (dB)
MLP postprocessedPSNR = 39.6552 (dB)
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Experiment & Result – cont. Expt #4: color image
train with a high bpp image, test with a low bpp image Training JPEG image bit rate = 0.374 bpp
JPEG (0.065 bpp)PSNR = 37.4064 (dB)
MLP postprocessedPSNR = 37.3664 (dB)
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Experiment & Result – cont. Expt #5:
train with a high bpp grayscale image, test with a low bpp color image
Training JPEG image bit rate = 0.255 bpp
JPEG (0.065 bpp)PSNR = 37.4064 (dB)
MLP postprocessedPSNR = 37.4312 (dB)
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Experiment & Result – cont. Expt #6:
train with a high bpp color image, test with a low bpp grayscale image
Training JPEG image bit rate = 0.255 bpp
JPEG (0.085 bpp)PSNR = 39.5696 (dB)
MLP postprocessedPSNR = 39.125 (dB)
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Conclusion
MLP can decrease blocking artifact from experiment #3 High quality image training data is
needed Current MLP structure does not suit
color image training data Further Study on the MLP structure
for color image
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Demo
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References W. B. Pennebaker and J. L. Mitchell, (1992) JPEG Still
Image Compression Standard. New York: Van Nostrand Reinhold.
Martin Boliek, Charilaos Christopoulos, Eric Majani, (2000) JPEG 2000 Image Coding System, ISO/IEC JTCI/SC29 WGI, http://www.jpeg.org/CDs15444.html
Guoping Qiu, (2000) MLP for Adaptive Postprocessing Block-Coded Images. IEEE Transactions On Circuits And Systems For Video Technology, Vol. 10, No. 8, December 2000
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Q&A