Correlation estimation and performance optimization for distributed image compression

Zhihai He; Lei Cao; Hui Cheng

doi:10.1117/12.641853

19 January 2006 Correlation estimation and performance optimization for distributed image compression

Zhihai He, Lei Cao, Hui Cheng

Proceedings Volume 6077, Visual Communications and Image Processing 2006; 60770R (2006) https://doi.org/10.1117/12.641853
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Correlation estimation plays a critical role in resource allocation and rate control for distributed data compression. A Wyner-Ziv encoder for distributed image compression is often considered as a lossy source encoder followed by a lossless Slepian-Wolf encoder. The source encoder consists of spatial transform, quantization, and bit plane extraction. In this work, we find that Gray code, which has been extensively used in digital modulation, is able to significantly improve the correlation between the source data and its side information. Theoretically, we analyze the behavior of Gray code within the context of distributed image compression. Using this theoretical model, we are able to efficiently allocate the bit budget and determine the code rate of the Slepian-Wolf encoder. Our experimental results demonstrate that the Gray code, coupled with accurate correlation estimation and rate control, significantly improves the picture quality, by up to 4 dB, over the existing methods for distributed image compression.

Citation Download Citation

Zhihai He, Lei Cao, and Hui Cheng "Correlation estimation and performance optimization for distributed image compression", Proc. SPIE 6077, Visual Communications and Image Processing 2006, 60770R (19 January 2006); https://doi.org/10.1117/12.641853

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