Intra prediction based on statistical modeling of images

Abstract

Intra prediction is an important part of intra-frame coding. A number of approaches have been proposed to improve intra prediction including a general linear prediction approach in which a weighted sum of all available neighbor pixels is used to predict each block pixel. An important part of this approach is the determination of the used weights. One method to determine the weights is to use the least-squares solution of an overdetermined linear system of weights. In this paper, we present an alternative approach where the weights are determined based on statistical modeling of image pixels. This approach results in an analytical expression for the weights and can achieve similar coding gains as methods based on least-squares solutions of overdetermined systems, while having several benefits such as reduced storage or computations.