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Proceedings Paper

Layered Wyner-Ziv video coding for transmission over unreliable channels
Author(s): Qian Xu; Vladimir Stankovic; Zixiang Xiong
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Paper Abstract

Based on recent works on Wyner-Ziv coding (or lossy source coding with decoder side information), we consider the case with noisy channel and addresses distributed joint source-channel coding, while targeting at the important application of scalable video transmission over wireless networks. In Wyner-Ziv coding, after quantization, Slepian-Wolf coding (SWC) is used to reduce the rate. SWC is traditionally realized by sending syndromes of a linear channel code. Since syndromes of the channel code can only compress but cannot protect, for transmission over noisy channels, additional error protection is needed. However, instead of using one channel code for SWC and one for error protection, our idea is to use a single channel code to achieve both compression and protection. We replace the traditional syndrome-based SWC scheme by the parity-based one, where only parity bits of the Slepian-Wolf channel code are sent. If the amount of transmitted parity bits increases above the Slepian-Wolf limit, the added redundancy is exploited to cope against the noise in the transmission channel. Using IRA codes for practical parity-based SWC, we design a novel layered Wyner-Ziv video coder which is robust to channel failures and thus very suitable for wireless communications. Our simulation results show great advantages of the proposed solution based on joint source-channel coding compared to the traditional approach where source and channel coding are performed separately.

Paper Details

Date Published: 31 July 2006
PDF: 12 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59602H (31 July 2006); doi: 10.1117/12.631565
Show Author Affiliations
Qian Xu, Texas A&M Univ. (United States)
Vladimir Stankovic, Texas A&M Univ. (United States)
Zixiang Xiong, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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