Predictive Vector Quantizer Design for Distributed Source Coding

This paper investigates the design of a system of predictive vector quantizers for distributed sources with memory, in which linear prediction is used to exploit the source memory, while distributed quantization is used to exploit the correlation between sources. A training-based algorithm is proposed for jointly designing the predictors, binning functions, and reconstruction codebooks of the given system to match the intra-and inter-source correlations. In order to demonstrate the effectiveness of the algorithm, experimental results obtained by designing both scalar and vector quantizers for a set of distributed Gauss-Markov sources are presented. While the optimality of these designs is unknown, it is shown that they convincingly outperform several other alternatives.