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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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LSSVC: A Learned Spatially Scalable Video Coding Scheme.

Yifan Bian, Xihua Sheng, Li Li

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    Summary
    This summary is machine-generated.

    This study introduces LSSVC, a novel learned spatially scalable video coding scheme. It significantly improves compression performance by leveraging base layer information for enhancement layers, outperforming existing standards.

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    Area of Science:

    • Computer Vision
    • Video Compression
    • Machine Learning

    Background:

    • Traditional block-based video coding has limitations in compression efficiency.
    • Learned video coding shows promise for improved performance.
    • Existing scalable video coding methods have limited room for further gains.

    Purpose of the Study:

    • To propose an end-to-end learned spatially scalable video coding (LSSVC) scheme.
    • To enhance compression performance in scalable video coding.
    • To provide a novel solution leveraging base layer information.

    Main Methods:

    • Developed an end-to-end learned spatially scalable video coding scheme (LSSVC).
    • Utilized base layer (BL) motion, texture, and latent information as interlayer information for the enhancement layer (EL).
    • Designed three modules: contextual motion vector (MV) encoder-decoder, hybrid temporal-layer context mining, and entropy model with BL latent priors.

    Main Results:

    • LSSVC demonstrated superior compression performance compared to H.265/SHVC.
    • The proposed modules effectively reduced interlayer redundancy.
    • Leveraging BL information improved high-resolution MV and latent compression.

    Conclusions:

    • LSSVC offers a significant advancement in learned spatially scalable video coding.
    • The integration of BL information is crucial for enhancing EL compression.
    • The proposed scheme provides a new state-of-the-art solution for scalable video compression.