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Updated: Nov 30, 2025

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Online Learning-Based Multi-Stage Complexity Control for Live Video Coding.

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    Summary
    This summary is machine-generated.

    This study introduces an online learning method for High Efficiency Video Coding (HEVC) complexity control. It enables accurate, adaptive complexity management for live video on mobile devices, balancing performance and power.

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    Area of Science:

    • Video Compression
    • Computer Vision
    • Machine Learning

    Background:

    • High Efficiency Video Coding (HEVC) offers superior compression over H.264/AVC but demands high computational resources.
    • Live video applications on mobile devices face challenges due to low-delay and power constraints.
    • Existing methods struggle to balance HEVC's efficiency with device limitations.

    Purpose of the Study:

    • To propose an online learning-based multi-stage complexity control method for HEVC live video coding.
    • To enable accurate complexity management adaptable to device computing capabilities.
    • To achieve an optimal trade-off between complexity control and rate-distortion (RD) performance.

    Main Methods:

    • Developed a three-stage method: multi-accuracy Coding Unit (CU) decision, multi-stage complexity allocation, and Coding Tree Unit (CTU) level complexity control.
    • Employed an online learning approach for the multi-accuracy CU decision model to adapt to diverse video characteristics.
    • Integrated CTU-level control to select optimal CU decision model accuracy for balancing complexity and RD performance.

    Main Results:

    • The proposed algorithm accurately controls coding complexity, reducing it from 100% to 40%.
    • Demonstrated superior performance compared to state-of-the-art algorithms in both complexity control accuracy and RD performance.
    • Successfully replaced brute-force search with an optimized CU size determination.

    Conclusions:

    • The online learning-based multi-stage complexity control method effectively addresses HEVC's computational demands for live video on mobile devices.
    • The algorithm provides accurate and adaptive complexity management, crucial for power-constrained environments.
    • Achieved significant improvements in rate-distortion performance while maintaining precise complexity control.