基于混合码本数据结构的改进自适应矢量量化算法

Hsiu-Niang Chen , Kuo-Liang Chung
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引用次数: 4

摘要

最近,Shen等[IEEE Transactions on Image Processing 2003; 12:283-95]提出了一种高效的自适应矢量量化(AVQ)算法,该算法的峰值信噪比(PSNR)优于之前的基准AVQ算法。本文在提出的混合码本数据结构的基础上,提出了一种改进的AVQ算法,该混合码本由三个码本组成:局域码本、静态码本和历史码本。由于易于维护的优点,本文提出的AVQ算法在保持与Shen等人先前的AVQ算法相似的PSNR性能的同时,具有相当大的计算节省效果。[IEEE Transactions on Image Processing 2003; 12:283-95]。实验结果表明,本文提出的AVQ算法与之前的AVQ算法相比,编码时间改进率约为75%,两种算法的PSNR性能相近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved adaptive vector quantization algorithm using hybrid codebook data structure

Recently, Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95] presented an efficient adaptive vector quantization (AVQ) algorithm and their proposed AVQ algorithm has a better peak signal-to-noise ratio (PSNR) than that of the previous benchmark AVQ algorithm. This paper presents an improved AVQ algorithm based on the proposed hybrid codebook data structure which consists of three codebooks—the locality codebook, the static codebook, and the history codebook. Due to easy maintenance advantage, the proposed AVQ algorithm leads to a considerable computation-saving effect while preserving the similar PSNR performance as in the previous AVQ algorithm by Shen et al. [IEEE Transactions on Image Processing 2003;12:283–95]. Experimental results show that the proposed AVQ algorithm over the previous AVQ algorithm has about 75% encoding time improvement ratio while both algorithms have the similar PSNR performance.

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