A tabu search based maximum descent algorithm for VQ codebook design

Abstract

A maximum descent (MD) method has been proposed for vector quantization (VQ) codebook design. Compared with the traditional generalized Lloyd algorithm (GLA), the MD algorithm achieves better codebook performance with far less computation time. However, searching for the optimal partitioning hyperplane of a multidimensional cluster is a difficult problem in the MD algorithm. Three partition techniques have been proposed for the MD method in the literature. In this paper, a new partition technique based on the tabu search (TS) approach is presented for the MD algorithm. Experimental results show that the tabu search based MD algorithm can produce a better codebook than can the conventional MD algorithms.