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Related Experiment Videos

Prediction by partial approximate matching for lossless image compression.

Yong Zhang1, Donald A Adjeroh

  • 1Center for Biotechnology and Informatics, Department of Radiology, The Methodist Research Institute, Houston, TX 77030-2707, USA. yzhang@tmhs.org

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|May 17, 2008
PubMed
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Prediction by Partial Approximate Matching (PPAM) improves lossless image compression by using approximate contexts, outperforming exact matching methods for images with common features like biomedical data.

Area of Science:

  • Computer Science
  • Information Theory
  • Image Processing

Background:

  • Context-based modeling is crucial for high-performance lossless data compression.
  • Defining and utilizing contexts for natural images presents challenges due to the vast number of contexts, increasing modeling costs and reducing compression efficiency.

Purpose of the Study:

  • To introduce a novel context modeling method for image compression inspired by text compression techniques.
  • To address the limitations of exact context matching in image compression.

Main Methods:

  • Proposed Prediction by Partial Approximate Matching (PPAM) method for image compression and context modeling.
  • Introduced the concept of approximate contexts, differing from the exact contexts used in Prediction by Partial Matching (PPM).

Related Experiment Videos

  • Modeled the probability of encoding symbols based on previous contexts, considering context occurrences approximately.

    • Main Results:

    • PPAM demonstrates competitive compression performance against popular lossless image compression algorithms.
    • Achieved superior performance particularly for images with shared characteristics, such as biomedical images.
    • The use of approximate contexts effectively managed the complexity of natural image contexts.

    Conclusions:

    • PPAM offers an effective approach to context modeling for lossless image compression.
    • The method provides a viable alternative to exact matching, especially for specific image types.
    • Approximate context matching balances modeling cost and compression efficiency effectively.