A Linear Model For Yuv 4:2:0 Chroma Intra Prediction

In video coding, although inter-channel redundancy has been somewhat decorrelated through the conversion of RGB-to-YUV, there are still some correlations among Y, Cb, and Cr components [1]. As a result, chroma components can be predicted from the luma component based on a linear model. The parameters of the linear model are estimated through a Least Square Solution (LSE) by using reconstructed neighboring chroma and the causal luma samples as training data at both the encoder and the decoder sides. However, it is difficult to derive the optimal parameters especially when there are insufficient training samples for prediction blocks with smaller sizes. To address this issue, we propose new method on deriving linear model and multi-model linear model based on up-sampling chroma samples (LM_UP and MMLM_UP) for chroma intra prediction. This proposed method performs consistently better than the state-of-the-art Joint Exploration Model version 7 (JEM-7.0) with an average of 0.06%, 0.31%, and 0.25% BD-rate reduction for Y, Cb, and Cr components, respectively, while the complexity increases at both encoder and decoder sides are negligible.