Constrained-storage multistage vector quantization based on genetic algorithms

摘要

Multistage vector quantization (MSVQ) and their variants have been recently proposed. Before MSVQ is designed, the user must artificially determine the number of codewords in each VQ stage. However, the users usually have no idea regarding the number of codewords in each VQ stage, and thus doubt whether the resulting MSVQ is optimal. This paper proposes the genetic design (GD) algorithm to design the MSVQ. The GD algorithm can automatically find the number of codewords to optimize each VQ stage according to the rate–distortion performance. Thus, the MSVQ based on the GD algorithm, namely MSVQ(GD), is proposed here. Furthermore, using a sharing codebook (SC) can further reduce the storage size of MSVQ. Combining numerous similar codewords in the VQ stages of MSVQ produces the codewords of the sharing codebook. This paper proposes the genetic merge (GM) algorithm to design the SC of MSVQ. Therefore, the constrained-storage MSVQ using a SC, namely CSMSVQ, is proposed and outperforms other MSVQs in the experiments presented here.