Distributed video coding with progressive correlation noise refinement and maximum likelihood pre-decoding

Juan Song; Yunsong Li; Keyan Wang; Chengke Wu; Bormin Huang

doi:10.1117/1.OE.51.7.077401

12 July 2012 Distributed video coding with progressive correlation noise refinement and maximum likelihood pre-decoding

Juan Song, Yunsong Li, Keyan Wang, Chengke Wu, Bormin Huang

Author Affiliations +

Optical Engineering, Vol. 51, Issue 7, 077401 (July 2012). https://doi.org/10.1117/1.OE.51.7.077401

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.

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Juan Song, Yunsong Li, Keyan Wang, Chengke Wu, and Bormin Huang "Distributed video coding with progressive correlation noise refinement and maximum likelihood pre-decoding," Optical Engineering 51(7), 077401 (12 July 2012). https://doi.org/10.1117/1.OE.51.7.077401

Published: 12 July 2012

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KEYWORDS

Video coding

Video

Computer programming

Quantization

Motion estimation

Quantum efficiency

Motion models

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