Index assignment in vector quantisation for channels with memory

Abstract

The study presents a formulation of a Hadamard framework for analysing the vector quantisation over channels with memory. In seeking faster response, classes of index assignments are defined in terms of the Hadamard transform of channel transition probabilities. An index assignment algorithm is developed that achieves high robustness against channel errors, and its performance in vector quantisation of Gauss-Markov sources under noisy channel conditions is illustrated.