An iterative joint codebook and classifier improvement algorithm for finite-state vector quantization

Abstract

A finite-state vector quantizer (FSVQ) is a multicodebook system in, which the current state (or codebook) is chosen as a function of the previously quantized vectors. The authors introduce a novel iterative algorithm for joint codebook and next state function design of full search finite-state vector quantizers. They consider the fixed-rate case, for which no optimal design strategy is known. A locally optimal set of codebooks is designed for the training data and then predecessors to the training vectors associated with each codebook are appropriately labelled and used in designing the classifier. The algorithm iterates between next state function and state codebook design until it arrives at a suitable solution. The proposed design consistently yields better performance than the traditional FSVQ design method (under identical state space and codebook constraints).

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