Context模型奇异测度及其在量化中的应用  

Amazing Measure of Context Model and its Application in Quantization

作　　者：陈旻[1] 王开云[2] 贾学明[1] 赵卿[3] 

机构地区：[1]云南警官学院信息网络安全学院,云南昆明650223 [2]昆明学院学报编辑部,云南昆明650214 [3]昆明学院信息技术学院,云南昆明650214

出　　处：《昆明学院学报》2015年第3期105-109,125,共6页Journal of Kunming University

基　　金：云南省自然科学基金青年基金资助项目(2013FD042);国家自然科学基金资助项目(61062005)

摘　　要：使用聚类算法实现Context量化不仅可以推广量化器的应用范围,而且可以获得编码性能较理想的优化量化器.然而,聚类算法依赖于相似测度.前期研究中采用的描述长度增量不能完全满足相似测度的各项属性,从而导致聚类结果的性能偏差.因此,提出数学描述特性更好的奇异测度增量作为两个计数向量的相似测度,并说明其相应性质.实验结果证明,使用奇异测度增量作为相似测度,不仅能够保证Context量化器的稳定性,而且还获得更佳的编码结果.The context quantization based on the clustering algorithm can not only improve the application range but also get the optimizing quantizer with ideal coding efficiency.However,the clustering algorithm relies on the similarity measure.In the previous research,the increment of the description length is not suitable for the various attributes of similarity measure so as to cause the deviation of cluster result.So the increment of amazing measure with better mathematic descriptive feature as the similarity measure of two count vector quantity is proposed and its corresponding quality is stated.The results indicate that the application of increment of amazing measure as the similarity measure can not only guarantee the stability of Context quantizer,but also achieve better coding efficiency.

关 键 词：Context建模 熵编码 描述长度 奇异测度 

分 类 号：TP919.1[自动化与计算机技术]