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Adaptive Chroma Prediction Based on Luma Difference for H.266/VVC.

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    This study introduces luma difference-based chroma prediction (LDCP) for enhanced H.266/VVC video coding. LDCP improves coding efficiency by using luma differences to adaptively weight chroma reference samples, achieving significant bitrate reductions.

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    Area of Science:

    • Video Compression Technology
    • Digital Signal Processing
    • Computer Vision

    Background:

    • Cross-component chroma prediction is crucial for efficient video coding in H.266/VVC.
    • Existing methods may not fully leverage the relationship between luma and chroma components.

    Purpose of the Study:

    • To develop an attention-based model for chroma prediction using luma differences.
    • To enhance coding efficiency in H.266/VVC through adaptive chroma weighting.

    Main Methods:

    • Designed a luma difference-based chroma prediction (LDCP) model utilizing an attention mechanism.
    • Employed luma differences (LDs) as input to a softmax function for nonlinear mapping to chroma weights.
    • Implemented adaptive weighting through template-based (T-LDCP) and offline learning (L-LDCP) approaches.

    Main Results:

    • T-LDCP achieved BD-rate reductions of 0.34% (Y), 2.02% (Cb), and 2.34% (Cr).
    • L-LDCP resulted in BD-rate savings of 0.32% (Y), 2.06% (Cb), and 2.21% (Cr).
    • L-LDCP introduced minor encoding (2%) and decoding (1%) time increases with minimal hardware-friendly parallelization benefits.

    Conclusions:

    • LDCP effectively improves chroma prediction accuracy and coding efficiency in H.266/VVC.
    • Both T-LDCP and L-LDCP offer significant bitrate savings with L-LDCP providing slightly better compression.
    • The LDCP method is hardware-friendly due to its pixel-level parallelization capabilities.