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Universal image compression using multiscale recurrent patterns with adaptive probability model.

E B de Lima Filho1, E B da Silva, M B de Carvalho

  • 1Centro de Ciencia, Technologia e Inovacao, Brazil. eddie@ctpim.org.br

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 9, 2008
PubMed
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This study enhances the multidimensional multiscale parser (MMP) algorithm for data compression. New methods improve compression of smooth images while maintaining performance on complex data.

Area of Science:

  • Computer Science
  • Signal Processing
  • Data Compression

Background:

  • The multidimensional multiscale parser (MMP) is a universal lossy compression method.
  • MMP utilizes approximate multiscale pattern matching and a self-updating dictionary.
  • Existing MMP has shown success in compressing images, video, and ECG signals.

Purpose of the Study:

  • To improve the MMP algorithm by incorporating source-specific knowledge.
  • To enhance compression efficiency for specific data types, such as smooth images.
  • To refine MMP's universal character by boosting performance on smooth data.

Main Methods:

  • Developed context models based on smoothness constraints for block probabilities.
  • Integrated knowledge of original block scales into the dictionary updating process.

Related Experiment Videos

  • Applied data-specific extensions to the base MMP algorithm.

    • Main Results:

    • Achieved significant compression improvements for smooth images compared to the original MMP.
    • Maintained state-of-the-art compression performance for complex, non-smooth images.
    • Demonstrated enhanced effectiveness of MMP with smoothness assumptions.

    Conclusions:

    • The enhanced MMP algorithm offers superior compression for smooth images.
    • The developed methods improve MMP's adaptability and performance across diverse data types.
    • Incorporating source knowledge strengthens the universal applicability of MMP.