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Updated: Jan 19, 2026

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Tunable VVC Frame Partitioning based on Lightweight Machine Learning.

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    Summary
    This summary is machine-generated.

    A new machine learning approach using Random Forest classifiers reduces video coding complexity. This method achieves significant encoding time reduction with minimal impact on compression efficiency for Versatile Video Coding (VVC).

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    Area of Science:

    • Computer Science
    • Signal Processing
    • Machine Learning

    Background:

    • Block partition structure is crucial for video compression efficiency.
    • Versatile Video Coding (VVC) introduces a Quad Tree Binary Tree (QTBT) structure, increasing encoding time.
    • High Efficiency Video Coding (HEVC) uses QT partitioning, while VVC adds BT partitions.

    Purpose of the Study:

    • To propose a lightweight and tunable QTBT partitioning scheme for VVC.
    • To reduce encoding complexity without significant loss in compression performance.
    • To enable a tunable trade-off between complexity reduction and coding loss.

    Main Methods:

    • Utilizing Random Forest classifiers to predict optimal block partition modes.
    • Implementing risk intervals to mitigate encoding loss from misclassifications.
    • Integrating the scheme into JEM-7.0 and VTM-5.0 software for evaluation.

    Main Results:

    • Achieved 30% average encoding complexity reduction in VVC with a 0.7%-3.0% BDBR increase.
    • Demonstrated 25%-61% average complexity reduction for MTT partitioning in VTM-5.0 with a 0.4%-2.2% BD-BR increase.
    • Showcased a tunable trade-off between complexity and coding loss via risk interval adjustment.

    Conclusions:

    • The proposed ML-based QTBT partitioning scheme effectively reduces video encoding complexity.
    • The solution offers a tunable balance between compression efficiency and computational cost.
    • The approach is efficient and applicable to both QTBT and MTT partitioning schemes in VVC.