Image compression using lossless coding on VQ indexes

Summary form only given. We first classify VQ indexes into smooth and non-smooth groups by using the relative variance of each code vector. Based on the smoothness of neighboring indexes, we define three different probability models. These models describe the correlations between the current VQ index and its neighboring indexes more precisely, and adaptive arithmetic coding schemes can be applied more efficiently. Furthermore, the size of each model is guaranteed not to be greater than the square of the number of code vectors, and there is no difficulty in implementation of arithmetic coding. In one of the models we generalize the idea of gradient match in Juan and Lee (1998) to predict the current index by use of its four known neighbors. We compare our method with conditional entropy coding of VQ indexes