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    Area of Science:

    • Computer Vision
    • Image Processing
    • Cloud Computing

    Background:

    • Increasing cloud image storage necessitates efficient compression techniques.
    • Exploiting inter-image correlations is key for effective image compression.

    Purpose of the Study:

    • To propose a novel image prediction scheme for cloud storage.
    • To leverage semi-local approaches for exploiting inter-image correlation.

    Main Methods:

    • Segmenting reference images into planar regions using local features and super-pixels.
    • Compensating for geometric and photometric disparities between matched image regions.
    • Generating multiple references and organizing them into a pseudo-sequence for differential encoding.

    Main Results:

    • The proposed semi-local method significantly improves rate-distortion performance.
    • Demonstrated superior compression efficiency compared to state-of-the-art inter-coding solutions.
    • Effective utilization of classical video coding tools for image compression.

    Conclusions:

    • The novel image prediction scheme offers substantial gains in image compression efficiency.
    • Semi-local correlation exploitation is a promising direction for cloud image storage optimization.
    • The method provides a competitive alternative to existing high efficiency video coding solutions.