Self-aware Cross-component Prediction Model Based on Template for Screen Content Coding

The current video coding techniques in the field of reducing redundancy between luma and chroma components have limitations, as they often overlook cross-component correlations. Previous research has employed linear and multi-model linear models to capture cross-component correlations, which are not tailored for screen content sequences. To address this issue, this paper proposes a self-aware cross-component prediction method based on template for screen content coding. With the neighboring reference samples, four prediction models are derived, and chroma prediction values at the template are calculated with the models. The sum of absolute transformed difference (SATD) cost between chroma prediction values and chroma reconstruction values at the template is computed for each model. Subsequently, the model with the lowest SATD cost is determined to be the selected model, which is used to generate prediction values for the current chroma block. Notably, the selected model is adaptively determined at both the encoder and decoder sides consistently, without signaling a model index. Experimental results show that the proposed method achieves 0.73%, 1.62% and 1.75% bit-rate savings on Y, U and V components respectively over ECM 6.0, for class TGM (Text and Graphics with Motion) under All-Intra (AI) configuration.