Multi-model based cross-component linear model chroma intra-prediction for video coding

Abstract

Cross-component Linear Model (CCLM) chroma intra prediction assumes a linear correlation between the luma and chroma components in a coding block. With this assumption, the chroma components can be predicted by LM mode, which utilizes the reconstructed neighbouring samples to derive parameters of the linear model by linear regression. This paper presents a multi-model CCLM (MM-CCLM) approach, which applies more than one linear models in a coding block. With MM-CCLM, reconstructed neighbouring luma and chroma samples of the current block are classified into several groups and each group is used as a training set to derive its own linear model. The reconstructed luma samples of the current block are also classified to use corresponding linear model to predict the associated chroma samples. Simulation results show that 0.26%, 1.89% and 1.96% BD rate savings on Y, Cb and Cr components are achieved for All Intra (AI) configurations in average. The proposed method has been adopted in the Joint Exploration Model (JEM) by Joint Video Exploration Team (JVET).