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Near-lossless image compression: minimum-entropy, constrained-error DPCM.

L Ke1, M W Marcellin

  • 1Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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This study introduces a near-lossless image compression method using differential pulse code modulation (DPCM). The technique minimizes prediction error entropy for efficient, high-fidelity image compression.

Area of Science:

  • Computer Science
  • Image Processing
  • Data Compression

Background:

  • Traditional image compression methods often involve information loss.
  • Achieving high compression ratios while maintaining image fidelity is a significant challenge.

Purpose of the Study:

  • To develop a near-lossless image compression scheme.
  • To minimize the entropy of the quantized prediction error sequence.

Main Methods:

  • Utilized a differential pulse code modulation (DPCM) system.
  • Defined a near-lossless criterion (d gray-level error per pixel).
  • Constructed trellises for allowable quantized prediction error sequences.
  • Developed a minimum entropy algorithm based on defined contexts for prediction error modeling.

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Main Results:

  • The proposed scheme achieves near-lossless compression.
  • The method effectively minimizes prediction error entropy.
  • Experimental results demonstrate the effectiveness of the approach.

Conclusions:

  • The presented DPCM-based scheme offers an effective solution for near-lossless image compression.
  • The entropy minimization technique enhances compression efficiency while preserving image quality.