A progressive codebook training algorithm for image vector quantization
Yu‐Chen Hu, Chin‐Chen Chang · 1999
In vector quantization schemes, a good performance codebook is required in both the encoding and the decoding procedures. In this paper, we propose a novel codebook design scheme using a tree structure. The study on the distribution of codewords in a codebook motivated us to design our algorithm. Generally, a good distribution of codewords will lead to good codebook performance, and vice versa. As the experimental results show, the method we propose can increase the image quality (PSNR) by about 1.214 dB and 1.240 dB with the codebook sizes 128 and 256, compared with the codebook generated by the LBG algorithm, respectively. Therefore, it is obvious that the proposed scheme provides a good approach to design better VQ codebooks for image compression.