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