MDDSP Literature Survey Presentation Eric Heinen A Comparison of Still-Image Compression Standards and Proposed Methods for Improving Lossy Image Quality MDDSP Literature Survey Presentation Eric Heinen

Project Goals Investigate existing methods for improving JPEG image quality Consider adapting these methods to JPEG2000 Novel approaches? Compare image quality of JPEG and JPEG2000 (with & without improvements) across Image set Compression ratios

Joint Thresholding and Quantizer Selection for JPEG Encoding (M Joint Thresholding and Quantizer Selection for JPEG Encoding (M. Crouse and K. Ramchandran, 1997) Optimize encoding without affecting decoding Rate-Distortion optimization framework

Improved JPEG Compression Using Human Visual System (HVS) Model (G Improved JPEG Compression Using Human Visual System (HVS) Model (G. Sreelekha and P. S. Sathidevi, 2005) Exploits contrast sensitivity of HVS Sinusoid at given frequency requires certain amount of contrast to elicit a response HVS is insensitive to very low and very high frequencies Contrast Sensitivity Function (CSF) CSF Thresholding Discard DCT coefficients below CSF value

Improved JPEG Compression Using Human Visual System (HVS) Model (G Improved JPEG Compression Using Human Visual System (HVS) Model (G. Sreelekha and P. S. Sathidevi, 2005) Also exploits masking property of HVS Vision less sensitive to local variation in brighter regions Compute luminance masking threshold based on block’s DC coefficient DCT coefficients below this threshold discarded Quantization after thresholding different from JPEG standard incompatible with JPEG standard decoder

JPEG2000 vs. JPG Arbitrary rectangular tile size allowed Color space transformation can be Lossless (YUV) or lossy (YCbCr) Discrete Wavelet Transform (DWT) More sophisticated entropy encoding Improvements to JPEG can be applied

Image Quality Assessment Full-reference image quality metrics (IQM) Estimate DMOS Convenient Popular IQMs Peak Signal to Noise Ratio (PSNR) Universal Image Quality Index Structural Similarity (SSIM)

Statistical Evaluation of Recent IQMs (H. R. Sheikh, M. F. Sabir, and A. C. Bovik, 2006) Image database Set of distortions JPEG, JPEG2000, White Noise, Gaussian Blur, simulated wireless channel Collect mean opinion scores 3 different performance metrics RMSE between IQM score and DMOS after non-linear regression IQM RMSE PSNR 13.4265 JND 10.2649 DCTune 16.2150 PQS 10.4221 NQM 11.4698 Fuzzy S7 15.3544 BSDM (S4) 9.7934 SSIM (MS) 9.3691 IFC 9.0007 VIF 8.2459

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