Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/83.777082 | IEEE Transactions on Image Processing |
Keywords | DocType | Volume |
vq codewords,block sizes,lossless coding,image coding,vq theoretical promise,high-order statistical dependency,vector quantization,vq indexes,vq image coders,16d vq codewords,high vq complexity,lossless compression,vq variant,conditional entropy coding,statistical context modeling,memoryless vq,16-dimensional vq codewords,memoryless systems,conditional entropy,vector quantisation,address-vq technique,rate distortion optimality,memoryless basic vq,bit rate,image sampling,signal source,vq index,source coding,vq coders,vq complexity,adaptive entropy,high-order sample correlations,memoryless vq codewords,entropy codes,rate distortion theory,address vq,rate-distortion performance,image compression,image coders,vq dimension,context modeling,source code,indexation,transform coding,rate distortion optimization,block codes,context model,entropy coding | Journal | 8 |
Issue | ISSN | ISBN |
8 | 1057-7149 | 0-8186-7761-9 |
Citations | PageRank | References |
6 | 0.78 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| xiaolin wu | 1 | 6 | 0.78 |
| jiang wen | 2 | 6 | 0.78 |
| Wing Hung Wong | 3 | 607 | 96.45 |