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Adaptive prediction trees for image compression.

John A Robinson1

  • 1Department of Electronics, University of York, Heslington, York YO10 5DD UK. jar11@ohm.york.ac.uk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 12, 2006
PubMed
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This study introduces adaptive prediction trees for efficient still-image compression. This novel predictive coding method outperforms existing lossy and lossless alternatives for various image types.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Current image compression methods often rely on block transforms or wavelets.
  • Achieving both high compression ratios and image fidelity remains a challenge.
  • General-purpose methods applicable to diverse image types are needed.

Purpose of the Study:

  • To present a novel, general-purpose method for still-image compression.
  • To demonstrate the efficacy of predictive coding as an alternative to transform and wavelet methods.
  • To achieve efficient lossy and lossless compression across various image types.

Main Methods:

  • Adaptive Prediction Trees (APTs) method developed.
  • Data ordered in a multicomponent binary pyramid.

Related Experiment Videos

  • Nonlinear predictor, hex-trees, and adaptive runlength/Huffman coders employed.
  • Exploitation of structural redundancies between color components.
  • Adaptive color palettization and order statistics prefiltering utilized.

    • Main Results:

    • The APT method achieved efficient lossy and lossless compression.
    • Performance was evaluated on a diverse set of photographs, graphics, textual, and mixed images.
    • The APT method outperformed standard lossless and lossy compression alternatives.
    • Predictive coding demonstrated viability against block transform and wavelet methods.

    Conclusions:

    • Adaptive Prediction Trees offer a complete, general-purpose solution for still-image compression.
    • Predictive coding is a viable and potentially superior alternative to transform and wavelet-based compression.
    • The method shows significant improvements over existing techniques for diverse image datasets.