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Image Coding Using Generalized Predictors Based on Sparsity and Geometric Transformations.

Luis F R Lucas, Nuno M M Rodrigues, Eduardo A B da Silva

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    |June 23, 2016
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    Summary
    This summary is machine-generated.

    This study proposes a new generalized intra prediction framework for video coding, enhancing coding efficiency by using adaptive linear filters with sparsity constraints. The method shows significant bitrate savings, especially for complex image areas.

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    Area of Science:

    • Video Coding
    • Digital Signal Processing
    • Image Compression

    Background:

    • Directional intra prediction is crucial for modern video coding standards.
    • Existing directional modes are simple linear predictors, limiting insights for design.
    • A novel interpretation views directional prediction as a specific linear prediction case.

    Purpose of the Study:

    • To develop a generalized intra prediction framework using adaptive linear filters.
    • To improve coding efficiency by incorporating sparsity constraints.
    • To investigate efficient sparse linear models for video prediction.

    Main Methods:

    • Reinterpreting directional prediction via linear filters and geometric transformations.
    • Replacing first-order filters with adaptive linear filters under sparsity constraints.
    • Employing algorithms like matching pursuit, LARS, LASSO, and elastic net for model estimation.

    Main Results:

    • Implementation and evaluation within the High Efficiency Video Coding (HEVC) standard.
    • Demonstrated advantage in images with complex features and textured regions.
    • Achieved higher average bitrate savings compared to existing sparse representation methods.

    Conclusions:

    • The proposed generalized intra prediction framework enhances coding efficiency.
    • Adaptive linear filters with sparsity constraints offer superior prediction performance.
    • This approach is particularly effective for complex visual content in video compression.