基于改进K均值的Context量化模型

Context quantization model based on improved K mean

为了提高图形编码系统压缩性能,可以通过使用Context模型来得到当前所要编码符号的概率。但是事实证明由于高阶Context模型很难在统计中有效收敛于信号的真实分布,结果使得编码效果降低,这就是所谓的"模型代价"问题。为了解决这一问题,一种有效的方法就是对高阶Context模型进行量化。由于Context量化问题与一般矢量量化问题相似,可以在设定合适的失真度量准则的条件下,使用聚类算法来实现Context量化。目前,K均值是使用比较广泛的一种聚类算法,但K均值算法必须具有确定的类数和选定的初始聚类中心。但在实际中,K均值往往难以准确界定,从而导致了聚类效果不佳。基于此,为了找到最佳的聚类数,本文提出采用聚类有效性函数来改进K均值聚类算法。

In order to improve the compression performance of the image coding system,the context model can be used to get the probability of the current coded symbol. However, it is proved that high-level context model is difficult to achieve the true distribution of the signal, as a result the effect of coding is reduced, which is the so-called ＂model cost＂ problem. Context quantization is an efficient method to deal with this problem. As the context quantification is similar to the general vector quantization problem, context quantization can be achieved by the clustering algorithm under the condition that a suitable distortion measure is defined. Currently, K-means is widely used as a clustering algorithm, but K-means algorithm is determined by the premise that the number of classes and the initial cluster centers are given. However, in fact, K-means is often difficult to be defined precisely, resulting in poor clustering effects. To obtain the best number of clustersin this paper, using cluster validity to improve K-means clustering.