<title>Tree-structured vector quantization design using adaptive genetic algorithms</title>

Abstract

Recently, vector quantization (VQ) has received considerable attention, and has become an effective tool for image compression because of its high compression ratio and simple decoding process. In order to reduce the computational complexity of searching and archiving, tree search can be used in codebook generation which is a major problem of VQ. The Codebook can be generated by a clustering algorithm that selects the most significant vectors of a training set in order to minimize the coding error when all the training set vectors are encoded. Genetic algorithm (GA), a global search method with high robustness, is very effective at finding optimal or near optimal solution to some complex and nonlinear problems. This paper presents a new technique for design a tree-structured vector quantizer using adaptive genetic algorithm. The difference between adaptive GA (AGA) and standard GA is that the probabilities of crossover and mutation of the former are varied depending on fitness values of solutions, thus prove the performance. Experimental results have shown that applying AGA to clustering can accurately locate the clustering centers. In this paper, AGA is used in tree-structured VQ to generate very node codebook. It is proved theoretically and experimentally that the reconstructed images generated by this method have high visual qualities.