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VMAF Oriented Perceptual Coding Based on Piecewise Metric Coupling.

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    This summary is machine-generated.

    This study introduces a new video coding method optimized for the Video Multimethod Assessment Fusion (VMAF) metric. The approach achieves significant bit savings by coupling VMAF with traditional distortion metrics for improved perceptual quality.

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    Area of Science:

    • Video Compression
    • Perceptual Quality Metrics
    • Machine Learning in Video Coding

    Background:

    • High video coding performance relies on rate-distortion optimization.
    • Conventional metrics like Sum of Squared Error (SSE) are widely used.
    • Video Multimethod Assessment Fusion (VMAF) offers superior correlation with human perception but lacks direct integration into coding.

    Purpose of the Study:

    • To develop a rate-distortion optimized coding method for VMAF.
    • To address the challenges of integrating frame-level VMAF into block-based video coding.
    • To enhance perceptual coding performance using VMAF.

    Main Methods:

    • Proposed a VMAF-oriented perceptual coding method using piecewise metric coupling.
    • Explored the correlation between VMAF and SSE.
    • Formulated a rate-distortion optimization model based on the VMAF-SSE correlation.
    • Developed an optimized block-based coding method for VMAF.

    Main Results:

    • Achieved an average bit saving of 3.61% for VMAF under HEVC low_delay_p configuration.
    • Achieved an average bit saving of 2.67% for VMAF under HEVC random_access_main configuration.
    • Demonstrated the effectiveness of the proposed VMAF-oriented coding method.

    Conclusions:

    • The proposed method successfully integrates VMAF into block-based video coding.
    • Piecewise metric coupling enables rate-distortion optimization for VMAF.
    • The approach offers significant bit savings while improving perceptual quality.