Intra prediction based on Markov process modeling of images

In our previous work, we proposed a new approach to intra prediction, in which we model image pixels with a separable first-order Markov process. The used Markov process is separable and therefore the developed method was only applied to intra prediction with vertical, horizontal and DC modes. In this paper, we extend our previous work by developing intra prediction methods based on non-separable Markov models and apply them to intra prediction along any angular direction. Compared to general linear prediction approaches, in which each block pixel is predicted using a weighted sum of all neighbor pixels of the block, the proposed approach uses much fewer independent parameters and thus offers reduced memory or computation requirements, while achieving similar coding gains.