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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Video Compression

    Background:

    • Deep learning models are increasingly used for video in-loop filtering.
    • Existing methods face challenges in joint spatial-temporal modeling and side information injection.
    • Optimizing reconstructed frame quality in video compression remains a key research area.

    Purpose of the Study:

    • To develop a deep learning-based in-loop filter addressing spatial-temporal modeling and side information injection.
    • To enhance the quality of reconstructed intra-frames and inter-frames in video coding.
    • To reduce the bitrate of compressed video sequences.

    Main Methods:

    • A Progressive Rethinking Network (PRN) for intra-frame coding, simulating human decision-making for spatial modeling.
    • A collaborative learning mechanism for inter-frame coding, enabling feature-level interaction with reference frames.
    • Extraction of intra-frame side information (partition map) and inter-frame side information (warped reference frame features).

    Main Results:

    • The PRN with intra-frame side information achieved a 9.0% average BD-rate reduction under All-intra (AI) configuration.
    • The PRN with inter-frame side information achieved 9.0%, 10.6%, and 8.0% average BD-rate reductions under Low-Delay B (LDB), Low-Delay P (LDP), and Random Access (RA) configurations, respectively.
    • Significant improvements in video compression efficiency were demonstrated compared to the HEVC baseline.

    Conclusions:

    • The proposed PRN effectively integrates spatial-temporal modeling and side information for improved video compression.
    • The network's ability to progressively rethink and collaboratively learn enhances reconstructed frame quality.
    • This deep learning approach offers a promising direction for next-generation video coding standards.