Network Distributed Quantization

We investigate the design of rate-distortion optimal quantizers for distributed compression in a network with multiple senders and receivers. In such network, several noisy observations of one or more unseen sources are separately encoded by each sender and the quantization indices transmitted to a number of receivers, which jointly decode all available transmissions with the help of side information locally available. The joint statistics of the source data, the noisy observations and the side information are known, and exploited in the quantizer design. A variety of lossless coders for the quantization indices, including ideal multiple-source Slepian-Wolf coders, are allowed. We present the optimality conditions such quantizers must satisfy, together with an extension of the Lloyd algorithm for a locally optimal design. Experimental results for distributed quantization of Gaussian sources confirm the high-rate quantization theory established in our previous work.