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Variational Decompression of Image Data From DjVu Encoded Files.

Martin Holler, Kamil S Kazimierski

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces variational decompression for DjVu files, improving image quality by addressing compression artifacts. It enables regularized inversion of the DjVu compression process for better digital document archival.

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    Area of Science:

    • Digital Image Processing
    • Document Archival Technologies
    • Data Compression Algorithms

    Background:

    • DjVu file format is prevalent in digital document archival.
    • DjVu employs layer separation and transform-based compression, leading to artifacts at high compression rates.
    • Standard decompression methods struggle with these artifacts.

    Purpose of the Study:

    • To develop variational decompression techniques for DjVu files.
    • To analyze DjVu compression for modeling data constraints.
    • To enable regularized inversion of the DjVu compression procedure.

    Main Methods:

    • In-depth analysis of the DjVu compression standard.
    • Modeling data constraints for variational decompression.
    • Implementation of total variation and total generalized variation regularization.
    • Development of routines for data constraints and transformation operators.

    Main Results:

    • Demonstrated a framework for variational decompression of DjVu files.
    • Evaluated performance using total variation and total generalized variation regularization.
    • Provided routines for data constraints and transformation operators.

    Conclusions:

    • Variational decompression offers a promising approach to mitigate DjVu compression artifacts.
    • Regularized inversion of the compression procedure enhances decompression quality.
    • The developed methods support improved digital document archival.