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    This study introduces an online-learning method to speed up screen content coding (SCC) by optimizing mode and coding unit (CU) decisions. The approach significantly reduces encoding time while maintaining high video quality.

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    Area of Science:

    • Computer Science
    • Information Technology
    • Digital Signal Processing

    Background:

    • Screen Content Coding (SCC) enhances High Efficiency Video Coding (HEVC) but increases complexity.
    • Efficient mode and coding unit (CU) decision-making is crucial for SCC performance.
    • Existing SCC methods often face trade-offs between speed and compression efficiency.

    Purpose of the Study:

    • To develop an online-learning approach for accelerating mode and CU size decisions in SCC.
    • To improve the efficiency of SCC by reducing computational complexity without significant bitrate increase.
    • To enhance the discoverability of SCC research through optimized content.

    Main Methods:

    • Feature extraction: corner points and distinct color count in CUs for Bayesian modeling.
    • Online learning models to predict and skip unnecessary coding modes.
    • Spatial correlation analysis of modes among neighboring CUs.
    • Scene change detection for adaptive model updates.
    • Bayesian decision rules for fast CU size selection.

    Main Results:

    • Achieved 36.69% encoding time reduction with a 1.08% Bjøntegaard delta bitrate (BDBR) increase in all-intra configuration.
    • Integrated approach reduced encoding time by 48.83% with a 1.78% BDBR increase.
    • Demonstrated effectiveness of online learning and feature extraction for SCC optimization.

    Conclusions:

    • The proposed online-learning algorithm significantly accelerates SCC by optimizing decision processes.
    • The method offers a favorable trade-off between encoding speed and bitrate efficiency.
    • This approach provides a valuable contribution to efficient screen content video compression.