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Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding.

N V Boulgouris1, D Tzovaras, M G Strintzis

  • 1Information Processing Laboratory, Department of Electrical and Computer Engineering Department, Aristotle University of Thessaloniki, Thessaloniki 54006, Greece.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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This study introduces advanced lifting schemes for lossless image compression, enhancing linear predictors nonlinearly. The developed methods significantly improve image decorrelation and outperform existing multiresolution-based lossless coding algorithms.

Area of Science:

  • Digital image processing
  • Data compression
  • Signal processing

Background:

  • Lossless image compression is crucial for preserving image fidelity.
  • Existing methods often face limitations in decorrelation efficiency.
  • Lifting schemes offer a flexible framework for wavelet transforms and compression.

Purpose of the Study:

  • To obtain optimal predictors for lifting schemes in general n-dimensional cases.
  • To apply these predictors for lossless still image compression using quincunx and row-column sampling.
  • To investigate context modeling and adaptive arithmetic coding for wavelet coefficients.

Main Methods:

  • Obtained optimal predictors for lifting schemes.
  • Applied nonlinear enhancement to linear predictors.

Related Experiment Videos

  • Utilized directional postprocessing for quincunx sampling and adaptive-length postprocessing for row-column sampling.
  • Investigated context modeling and adaptive arithmetic coding of wavelet coefficients.

    • Main Results:

    • Achieved highly efficient image decorrelation through nonlinear interpolation schemes.
    • Both directional and adaptive-length postprocessing methods demonstrated strong performance.
    • The developed context modeling and adaptive arithmetic coding improved lossless compression.
    • Experimental results showed superior performance compared to known multiresolution-based algorithms.

    Conclusions:

    • The proposed nonlinear lifting schemes and associated postprocessing techniques are effective for lossless image compression.
    • Context modeling and adaptive arithmetic coding further enhance compression efficiency.
    • The developed approach represents a significant advancement in multiresolution-based lossless image coding.