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.

 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)

Experiment & Result  Optimal MLP structure after testing Structure: 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 = (dB) MLP postprocessed PSNR = (dB)

Experiment & Result – cont.  Expt #2: color image train and test with the same image JPEG (0.18 bpp) PSNR = (dB) MLP postprocessed PSNR = (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 = (dB) MLP postprocessed PSNR = (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 = bpp JPEG (0.065 bpp) PSNR = (dB) MLP postprocessed PSNR = (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 = bpp JPEG (0.065 bpp) PSNR = (dB) MLP postprocessed PSNR = (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 = bpp JPEG (0.085 bpp) PSNR = (dB) MLP postprocessed PSNR = (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,  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