Wu X, Wen J, Wong W H
Dept. of Comput. Sci., Univ. of Western Ontario, London, Ont., Canada.
IEEE Trans Image Process. 1999;8(8):1005-13. doi: 10.1109/83.777082.
Block sizes of practical vector quantization (VQ) image coders are not large enough to exploit all high-order statistical dependencies among pixels. Therefore, adaptive entropy coding of VQ indexes via statistical context modeling can significantly reduce the bit rate of VQ coders for given distortion. Address VQ was a pioneer work in this direction. In this paper we develop a framework of conditional entropy coding of VQ indexes (CECOVI) based on a simple Bayesian-type method of estimating probabilities conditioned on causal contexts, CECOVI is conceptually cleaner and algorithmically more efficient than address VQ, with address-VQ technique being its special case. It reduces the bit rate of address VQ by more than 20% for the same distortion, and does so at only a tiny fraction of address VQ's computational cost.
实际的矢量量化（VQ）图像编码器的块大小不足以利用像素间所有的高阶统计相关性。因此，通过统计上下文建模对VQ索引进行自适应熵编码，可以在给定失真的情况下显著降低VQ编码器的比特率。地址VQ是这一方向上的开创性工作。在本文中，我们基于一种简单的贝叶斯型方法开发了一个VQ索引条件熵编码（CECOVI）框架，该方法用于估计基于因果上下文的概率。CECOVI在概念上比地址VQ更清晰，算法上更高效，地址VQ技术是其特殊情况。在相同失真情况下，它将地址VQ的比特率降低了20%以上，并且其计算成本仅为地址VQ的极小一部分。
