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    We introduce a deformable Wiener Filter (DWF) for hybrid video coding. This new in-loop filter improves noise reduction by combining local and non-local image features, outperforming current methods.

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    Area of Science:

    • Digital image processing
    • Video compression algorithms
    • Signal processing

    Background:

    • In-loop filters are crucial for noise reduction in hybrid video coding.
    • Current Versatile Video Coding (VVC) filters primarily use local image similarity.
    • Existing non-local filters are limited by unsupervised parameter estimation.

    Purpose of the Study:

    • To develop an advanced in-loop filter that integrates both local and non-local image characteristics.
    • To enhance noise reduction performance in video coding beyond current limitations.
    • To improve the efficiency and effectiveness of parameter estimation in in-loop filters.

    Main Methods:

    • Proposed a Deformable Wiener Filter (DWF) combining local and non-local sample information.
    • Supervisedly trained filter coefficients using Wiener Filter theory.
    • Classified samples into groups based on noise and characteristics for shared coefficients.
    • Adaptively fused local and non-local samples and applied outlier-constrained filtering.

    Main Results:

    • The DWF effectively combines local and non-local features for improved filtering.
    • Achieved significant bit-rate savings: 1.16% (All Intra), 1.92% (Random Access), 2.67% (Low-Delay B) compared to VTM-11.0.
    • Demonstrated superior performance across different configurations.

    Conclusions:

    • The Deformable Wiener Filter offers a novel and effective approach to in-loop filtering in video coding.
    • DWF surpasses existing methods by leveraging supervised learning and hybrid feature utilization.
    • The proposed method provides substantial coding efficiency gains, particularly in challenging video sequences.