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    This study introduces a new video compression network (TCVC-Net) that leverages long-term temporal context for enhanced performance. The novel approach improves coding efficiency and visual quality in video compression.

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    Area of Science:

    • Computer Science
    • Information Technology
    • Signal Processing

    Background:

    • Video compression relies on exploiting temporal redundancy between frames.
    • Current learned video compression methods often use short-term correlations, limiting performance.
    • Image-based codecs are insufficient for optimal video compression.

    Purpose of the Study:

    • To develop a novel video compression network (TCVC-Net) for improved coding performance.
    • To enhance the exploitation of temporal context in learned video compression.
    • To overcome limitations of existing short-term temporal correlation methods.

    Main Methods:

    • Proposed a temporal context-based video compression network (TCVC-Net).
    • Introduced a global temporal reference aggregation (GTRA) module for long-term context.
    • Developed a temporal conditional codec (TCC) for motion vector and residue compression.

    Main Results:

    • TCVC-Net aggregates long-term temporal context for accurate motion-compensated prediction.
    • The temporal conditional codec (TCC) preserves structural and detailed information.
    • Experimental results demonstrate superior performance over state-of-the-art methods.

    Conclusions:

    • TCVC-Net significantly improves learned video compression performance.
    • Exploiting long-term temporal context is crucial for advanced video coding.
    • The proposed methods achieve better PSNR and MS-SSIM metrics.