Density-based image vector quantization using a genetic algorithm

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.