Quantization Splitting For Symmetric K-Channel Multiple Descriptions

We propose a new coding scheme for the symmetric $K$-channel multiple description problem based on the quantization splitting technique, which was previously successfully applied to the Gaussian CEO problem. Unlike a coding scheme we discovered earlier, the scheme proposed here can provide performance better than the one by Pradhan, Puri and Ramchandran in a component-wise manner. Though the method is conceptually straightforward once the analogy to the Gaussian CEO coding scheme is made, the general coding scheme requires constraining the space of the splitting random variables in a much more delicate way. We provide a set of conditions for a specific choice of splitting structure to yield valid splitting random variables.