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A Discrete-Mapping-Based Cross-Component Prediction Paradigm for Screen Content Coding
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AI
DOI：10.1109/TIP.2023.3334970.png)
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摘要
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Cross-component prediction is an important intra-prediction tool in the modern video coders. Existing prediction methods to exploit cross-component correlation include cross-component linear model and its extension of multi-model linear model. These models are designed for camera captured content. For screen content coding, where videos exhibit different signal characteristics, a cross-component prediction model tailored to their characteristics is desirable. As a pioneering work, we propose a discrete-mapping based cross-component prediction model for screen content coding. Our model relies on the core observation that, screen content videos typically comprise of regions with a few distinct colors and luma value (almost always) uniquely conveys chroma value. Based on this, the proposed method learns a discrete-mapping function from available reconstructed luma-chroma pairs and uses this function to derive chroma prediction from the co-located luma samples. To achieve higher accuracy, a multi-filter approach is employed to derive co-located luma values. The proposed method achieves 2.61%, 3.51% and 3.92% Y, U and V bit-rate savings respectively over Enhanced Compression Model (ECM) 4.0, with negligible complexity, for text and graphics media under all-intra configuration.
Keyword:
Cross-component prediction
screen content coding
ECM
VVC
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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