A novel Slepian-Wolf decoding algorithm exploiting geometric regularity constraints with anisotropic MRF modeling

Yongsheng Zhang, Hongkai Xiong, Chang Wen Chen

Abstract: This paper proposes a novel Slepian-Wolf decoding algorithm for distributed video coding by exploiting not only the statistical correlation between the side-information and source but also the spatio-temporal consistency constraint of video sequences. The proposed algorithm models the log-likelihood-ratio (LLR) information for Slepian-Wolf decoding as an anisotropic MRF model and solving the inference by iteratively performing conventional probabilistic Slepian-Wolf decoding, which imposes the global bit-wise constraint from the Slepian-Wolf encoding process, and MRF optimization with belief propagation (BP) to enforce the local geometric regularity constraint of video frames. Experimental results demonstrate a considerable performance gain beyond existing Slepian-Wolf decoding algorithms in literature.