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Optical Engineering

Distributed video coding with progressive correlation noise refinement and maximum likelihood pre-decoding
Author(s): Juan Song; Yunsong Li; Keyan Wang; Chengke Wu; Bormin Huang
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Paper Abstract

Distributed video coding (DVC) reduces the complexity at the encoder by using source statistics at the decoder. However, a performance gap still exists between DVC and hybrid video coders due to the inaccuracy of correlation noise (CN) modeling and inefficient exploitation of side information. We propose improvements to the performance of DVC using the two following aspects. Firstly, a progressive refinement method is proposed to improve the accuracy of CN modeling for transform domain Wyner-Ziv video coding, in which previously decoded bitplanes are exploited to progressively refine estimated CN as bitplane decoding proceeds. Secondly, a maximum likelihood pre-decoding method is also proposed to obtain a further reduction in bitrate. In our method, bitplanes are pre-decoded first using the conditional bit probability without syndrome bits. The bitrate is reduced by avoiding requests for syndrome bits for the bitplanes having strong temporal correlation. The experimental results show that our proposed methods could provide bitrate savings up to 8.26% and PSNR gains up to 0.2 dB without a significant increase in decoding complexity.

Paper Details

Date Published: 12 July 2012
PDF: 13 pages
Opt. Eng. 51(7) 077401 doi: 10.1117/1.OE.51.7.077401
Published in: Optical Engineering Volume 51, Issue 7
Show Author Affiliations
Juan Song, Xidian Univ. (China)
Yunsong Li, Xidian Univ. (China)
Keyan Wang, Xidian Univ. (China)
Chengke Wu, Xidian Univ. (China)
Bormin Huang, Univ. of Wisconsin-Madison (United States)


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