Enhanced Cross-Component Linear Model for Chroma Intra-Prediction in Video Coding.

Abstract

Cross-Component Linear Model (CCLM) for chroma intra-prediction is a promising coding tool in Joint Exploration Model (JEM) developed by the Joint Video Exploration Team (JVET). CCLM assumes a linear correlation between the luma and chroma components in a coding block. With this assumption, the chroma components can be predicted by the Linear Model (LM) mode, which utilizes the reconstructed neighbouring samples to derive parameters of a linear model by linear regression. This paper presents three new methods to further improve the coding efficiency of CCLM. First, we introduce a multi-model CCLM (MM-CCLM) approach, which applies more than one linear models to a coding block. With MM-CCLM, reconstructed neighbouring luma and chroma samples of the current block are classified into several groups, and a particular set of linear model parameters is derived for each group. The reconstructed luma samples of the current block are also classified to predict the associated chroma samples with the corresponding linear model. Second, we propose a multi-filter CCLM (MF-CCLM) technique, which allows the encoder to select the optimal down-sampling filter for the luma component with the 4:2:0 colour format. Third, we present a LM-angular prediction (LAP) method, which synthesizes the angular intra-prediction and the MM-CCLM intra-prediction into a new chroma intra coding mode. Simulation results show that 0.55%, 4.66% and 5.08% BD rate savings in average on Y, Cb and Cr components respectively, are achieved for All Intra (AI) configurations with the proposed three methods. MM-CCLM and MF-CCLM have been adopted into the JEM by JVET.