Post-processing of JPEG image using MLP Fall 2003 ECE539 Final Project Report Data Fok.
-
Upload
brice-rice -
Category
Documents
-
view
220 -
download
0
Transcript of Post-processing of JPEG image using MLP Fall 2003 ECE539 Final Project Report Data Fok.
Post-processing of JPEG image using MLP
Fall 2003 ECE539 Final Project Report Data Fok
Overview Introduction
Approach
Experiments & Results
Conclusion
Demo
Introduction
Increase demand on graphic usage Graphics: large file size JPEG compression blocking artifact Unpopularity of JPEG 2000 Removal of JPEG artifact
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
Approach – cont.
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
Approach – cont.
Image quality evaluate by Human eyes Peak signal to noise ratio (PSNR)
MSEPSNR
255log10 10
2
,
2),(ˆ),(
MN
yxIyxI
MSE yx
Experiment & Result
Optimal MLP structure after testing
Structure: 15-5-6
Learning rate = 0.01
Momentum = 0.7
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)
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)
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)
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)
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)
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)
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
Demo
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
Q&A