Density-based image vector quantization using a genetic algorithm

Authors:
Chin-Chen Chang;Chih-Yang Lin
Affiliations:
Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan, R.O.C;Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan, R.O.C.
Venue:
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Year:
2007

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Abstract

Vector quantization (VQ) is a commonly used method in the compression of images and signals. The quality of VQ-encoded images heavily depends on the quality of the codebook. Conventional codebook training techniques are all based on the LBG (Linde-Buzo-Gray) method. However, LBG-based methods are noise sensitive and are not able to handle clusters of different shapes, sizes, and densities. In this paper, we propose a density-based clustering method that can identify arbitrary data shapes and exclude noises for codebook training. In order to rapidly approach an optimal solution, an improved version of a genetic algorithm is designed that demonstrates efficient initialization of codewords selection, crossover, and mutation. The experiments show that the proposed method is more robust in generating a common codebook than other LBG-based methods.