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    Area of Science:

    • Computer Science
    • Image Processing
    • Cloud Computing

    Background:

    • Social media platforms face high storage costs due to massive daily image uploads.
    • A significant asymmetry exists between uploaded and downloaded images, with most images rarely retrieved.

    Purpose of the Study:

    • To propose a cloud storage system that significantly reduces storage costs for JPEG images.
    • To minimize storage while maintaining image quality through intelligent re-encoding and retrieval.

    Main Methods:

    • Selective re-encoding of JPEG image blocks using coarser quantization parameters.
    • Exploiting sparsity and graph-signal smoothness priors for reverse mapping during download.
    • Utilizing tree-structured vector quantization for block clustering and tailored dictionaries/graphs.
    • Employing differential distributed source coding for side information transmission.

    Main Results:

    • Achieved significant storage savings, up to 12.05%.
    • Maintained image Peak Signal-to-Noise Ratio (PSNR) within 0.18 dB of the original.
    • Demonstrated effective lossless and near-lossless image recovery.

    Conclusions:

    • The proposed system offers a practical solution for reducing cloud storage costs for user-generated images.
    • The trade-off between storage reduction and computational cost during download is manageable.
    • The method effectively balances storage efficiency with image fidelity.