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    Area of Science:

    • Computer Vision
    • Image Processing
    • Data Compression

    Background:

    • Lossless predictive coding of images relies on adaptive predictors.
    • Existing methods often fail at object boundaries due to the local consistency assumption.
    • This limitation leads to significant predictive errors in natural images.

    Purpose of the Study:

    • To develop a novel adaptive predictor for lossless image coding.
    • To address limitations of current methods by incorporating patch redundancy alongside local consistency.
    • To enhance prediction accuracy for natural images, particularly those with textures and object boundaries.

    Main Methods:

    • Proposed a novel approach assuming simultaneous local consistency and patch redundancy in natural images.
    • Derived a family of linear models and developed an algorithm for automatic model selection.
    • Integrated traditional training evidence (local consistency) with novel target evidence (patch redundancy) using a Bayesian framework.

    Main Results:

    • The proposed predictor effectively combines local consistency and patch redundancy.
    • Achieved higher prediction efficiency compared to state-of-the-art lossless predictors.
    • Demonstrated superior performance on natural images with textures and object boundaries.

    Conclusions:

    • The novel adaptive predictor offers improved efficiency for lossless image coding.
    • Jointly exploiting local consistency and patch redundancy enhances prediction accuracy.
    • The method is particularly effective for complex image content, outperforming existing techniques.