Adaptive nonasymmetric Slepian-Wolf decoding using particle filtering based belief propagation
Samuel S. M. Cheng, Shuang Wang, Lijuan Cui · 2010
A major difficulty that plagues the practical use of Slepian-Wolf coding (and distributed source coding in general) is that the precise correlation among sources need to be known a priori. To resolve this problem, we have proposed an adaptive asymmetric Slepian-Wolf decoding scheme using particle filtering based belief propagation in our recent work. In this paper, we extend the adaptive scheme to the non-asymmetric setup based on the code partitioning approach. We show through experiments that the proposed algorithm can simultaneously reconstruct the compressed sources and estimate the joint correlation among sources. Further, comparing to the conventional Slepian-Wolf decoder based on standard belief propagation, the proposed approach can achieve higher compression under varying correlation statistics.