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    This study introduces a new video rescaling framework that uses contrastive learning to retain information during downscaling and a selective global aggregation module to capture long-range relationships, improving video quality and compression.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Digital Signal Processing

    Background:

    • Video rescaling methods jointly optimize downscaling and upscaling, but information loss during downscaling makes upscaling ill-posed.
    • Existing methods often use convolution, limiting their ability to capture long-range dependencies in videos.

    Purpose of the Study:

    • To propose a unified video rescaling framework addressing information loss and long-range dependency issues.
    • To enhance video compression and quality through improved rescaling techniques.

    Main Methods:

    • A contrastive learning framework is employed to regularize downscaled video information, synthesizing hard negative samples online.
    • A selective global aggregation module (SGAM) is introduced to efficiently capture long-range redundancy using sparse self-attention (SA).

    Main Results:

    • The proposed framework, CLSA (Contrastive Learning with Selective Aggregation), demonstrates superior performance over existing video rescaling and compression methods.
    • Experiments on five datasets confirm CLSA's state-of-the-art results in video rescaling.

    Conclusions:

    • The developed CLSA framework effectively mitigates information loss in video rescaling.
    • The integration of contrastive learning and SGAM offers a powerful approach for advanced video compression and quality enhancement.