Side-information-adaptive distributed source coding

Abstract

Consider distributed source coding in which each block of the source at the encoder is associated with multiple candidates for side information at the decoder, just one of which is statistically dependent on the source block. Our encoder codes the source as syndrome bits and also sends a portion of it uncoded as doping bits. The decoder adaptively discovers the best side information candidates for each block of the source. The main contribution is a method based on density evolution to analyze and design the coding performance. Experimental results show that the density evolution technique is accurate in modeling the codec and optimizing its doping rate.