Convolutional Cross-Component Models for Chroma Prediction in Video Coding

In this paper we present two novel approaches for improving intra and inter chroma prediction in video coding. Our research demonstrates that treating the cross-component predictor as a two-dimensional convolutional model can significantly enhance chroma prediction performance. The proposed two convolutional models incorporate multiple spatial neighbors, a bias term, and a nonlinear term. For intra-coded blocks, we derive the model coefficients on the reconstructed neighborhood of the block, while for inter-coded blocks, the model coefficients are determined using prediction samples. To evaluate our methods, we implemented them on top of the ECM software that is currently under exploration by the ITU-T/ISO/IEC Joint Video Experts Team. Our intra cross-component predictor achieves BD-rate savings of {-1.47%, -2.90%, -3.02%}, {-0.92%, -2.04%, -2.32%} (Y, U, V) for the all intra and the random access configurations over ECM-5.0, respectively. Our inter cross-component predictor achieves BD-rate savings of {-0.09%, -1.25%, -1.46%}, {-0.04%, -3.42%, -3.85%} for the random access and the low-delay B configurations over ECM-9.0, respectively. Both proposed methods have been adopted into the ECM software.