New codebook design algorithm for image vector quantization based on Kernel density estimation

A main problem in vector quantization (VQ) is codebook designation. The traditional method used for VQ codebook generation, is the Generalized Lloyd Algorithm (GLA). The efficiency of the GLA algorithm is hardly dependent on the initial codebook selection. But, GLA algorithm usually gets trapped into local minimum of distortion, resulting in a random codebook initialization. In this paper, an effective codebook initialization algorithm based on Kernel density estimation has been proposed. Experimental results show that the proposed algorithm not only improves the quality of generated codebook but decreases the computation time compared to the GLA algorithm.