A Power-Law Transformation Approach for Template-Based Cross-Component Prediction

While current cross-component chroma prediction tools have achieved significant performance improvements, they still face challenges in handling the non-linear relationship between luma and chroma. The paper proposes a template-derived power-law cross-component prediction model, with the key advantage of improving the numerical distribution characteristics of pixels in an interpretable manner. It effectively compresses cross-component redundancy while avoiding overfitting. The model achieves BD-Rate gains of −0.03%, −1.33%, −1.38% under the All-Intra configuration.