A COMPARATIVE STUDY TO DESIGN A CODE BOOK FOR VECTOR QUANTIZATION

摘要

In this paper, we examined six algorithms to construct an optimal code book (CB) for vector quantization (VQ) experimentally. Four algorithms are GLA (generalized Lloyd algorithm), FCM (fuzzy c meams), GA (genetic algorithm), and AP (affinity propagation). The other two algorithms are hybrid methods: AP+GLA and GA+FCM. Performance of the algorithms was evaluated by both PSNR (peak-signal-to-noise-ratio) and NPIQM (normalized perceptual image quality measure) of decoded images. Computational experiments showed that the performance of each algorithm could be categorized as higher performance and lower performance. GLA, AP and AP+GLA belong to the higher performance group, while FCM, GA and GA+FCM belong to the lower performance group. AP+GLA shows the best performance of algorithms in the higher performance group. Thus, AP+GLA is an optimal algorithm for constructing a CB for VQ.