基于Context量化的Context模型

 Context模型广泛地使用在图像编码系统中以提高压缩性能.然而,如果没有特殊的处理,预期的压缩收益将会由于高阶context模型引入的模型代价而被抵消.Context量化是解决这个问题的有效方法.详细分析了一般context量化存在的问题,提出context量化与一般矢量量化问题的相似性,并且证明了,如果设定了一个合适的失真度量准则,运用Lloyd迭代算法可设计出最优的context量化器.

Context modeling based on context quantization

Context modeling is widely used in image coding to improve the compression performance.However, with no special treatment, the expected compression gain will be cancelled by the model cost introduced by high order context models.Context quantization is an efficient method to deal with this problem.The general context quantization problem is analyzed in detail and showed that context quantization is similar to a common vector quantizer can be designed by a Lloyd style iterative algorithm.