Post processing of jpeg image using multilayer perceptron

1. Post-processing of JPEG
image using MLP
Fall 2003 ECE539
Final Project Report
Data Fok

2. Overview
 Introduction
 Approach
 Experiments & Results
 Conclusion
 Demo

3. Introduction
 Increase demand on graphic usage
 Graphics: large file size
 JPEG compression blocking artifact
 Unpopularity of JPEG 2000
 Removal of JPEG artifact

4. 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

5. Approach – cont.

6. 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

7. Approach – cont.
 Image quality evaluate by
 Human eyes
 Peak signal to noise ratio (PSNR)
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8. Experiment & Result
 Optimal MLP structure after testing
 Structure: 15-5-6
 Learning rate = 0.01
 Momentum = 0.7

9. Experiment & Result – cont.
 Expt #1: grayscale image
 train and test with the same image
JPEG (0.14 bpp)
PSNR = 41.2044 (dB)
MLP postprocessed
PSNR = 40.2514 (dB)

10. Experiment & Result – cont.
 Expt #2: color image
 train and test with the same image
JPEG (0.18 bpp)
PSNR = 38.2464 (dB)
MLP postprocessed
PSNR = 37.9718 (dB)

11. 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 postprocessed
PSNR = 39.6552 (dB)

12. 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 postprocessed
PSNR = 37.3664 (dB)

13. 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 postprocessed
PSNR = 37.4312 (dB)

14. 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 postprocessed
PSNR = 39.125 (dB)

15. 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

16. Demo

17. 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

18. Q&A