Abstract
Genetic algorithm (GA) has been successfully applied to codebook design for vector quantization (VQ). However, most conventional GA-based codebook design methods need long runtime because candidate solutions must be fine tuned by LBG. In this paper, a partition-based GA is applied to codebook design, which is referred to as genetic vector quantization (GVQ). In addition, simulated annealing (SA) algorithm is also used in GVQ to get more promising results and the corresponding method is referred to as GSAVQ. Both GVQ and GSAVQ use the linear scaling technique during the calculation of objective functions and use special crossover and mutation operations in order to obtain better codebooks in much shorter CPU time. Experimental results show that both of them save more than 71–87% CPU time compared to LBG. For different codebook sizes, GVQ outperforms LBG by 1.1–2.1 dB in PSNR, and GSAVQ outperforms LBG by 1.2–2.2 dB in PSNR. In addition, GVQ and GSAVQ need a little longer CPU time than, the maximum decent (MD) algorithm, but they outperform MD by 0.2–0.5 dB in PSNR.
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