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Invertibility-Driven Interpolation Filter for Video Coding.

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    This study introduces an invertibility-driven approach for fractional interpolation filters in video coding. The novel method, using convolutional neural networks, improves video compression efficiency and reduces bitrate.

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    Area of Science:

    • Digital Signal Processing
    • Computer Vision
    • Machine Learning

    Background:

    • Fractional interpolation filters are crucial for motion compensation in video coding standards.
    • Traditional filters struggle with non-stationary video content and varying quality due to band-limited assumptions.
    • Existing methods lack adaptability and efficient handling of fractional sample generation.

    Purpose of the Study:

    • To introduce the concept of invertibility for fractional interpolation filters.
    • To develop a learning-based method for designing adaptive and effective interpolation filters.
    • To improve video compression efficiency by enhancing motion compensation.

    Main Methods:

    • The study theoretically establishes the equivalence between spatial domain invertibility and constant magnitude in the Fourier transform domain.
    • A convolutional neural network (CNN) based end-to-end scheme is proposed to train invertibility-driven interpolation filters (InvIF).
    • The training scheme does not require hand-crafted ground truth fractional samples, simplifying the process.

    Main Results:

    • The proposed InvIF was integrated into High Efficiency Video Coding (HEVC).
    • Experiments demonstrated significant BD-rate reduction: 4.7% for low-delay-B and 3.6% for random-access configurations.
    • The invertibility-driven approach effectively captures video content properties and adapts to quality variations.

    Conclusions:

    • The invertibility property provides a robust theoretical foundation for designing fractional interpolation filters.
    • The learning-based InvIF method offers superior performance and adaptability compared to traditional filters.
    • This research contributes to more efficient video compression technologies.