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Image compression by texture modeling in the wavelet domain.

T W Ryan1, L D Sanders, H D Fisher

  • 1Sci. Applications Int. Corp., Tucson, AZ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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This study introduces a hybrid spectral texture modeling technique for advanced image compression, achieving lower bit rates by reconstructing less critical image areas as texture models.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Existing image compression algorithms achieve rates of 0.3-0.5 b/pixel with minimal quality loss.
  • Achieving lower bit rates requires alternative approaches beyond standard RMS error metrics.

Purpose of the Study:

  • To develop a novel image compression system capable of ultra-low bit rates.
  • To explore texture modeling for reconstructing less critical image regions.

Main Methods:

  • A hybrid spectral texture modeling technique is employed in the discrete wavelet transform domain.
  • Less critical image portions are transmitted as texture models and reconstructed with statistical and absolute fidelity.
  • This method avoids boundary blending issues common in polygonal modeling.

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Main Results:

  • The hybrid approach enables significantly lower transmission or storage rates.
  • Natural spectral texture models are generated, improving compression efficiency.
  • Reconstruction offers statistical fidelity in mid- to high-spatial frequencies and absolute fidelity in lowpass frequencies.

Conclusions:

  • The described hybrid compression system effectively achieves ultra-low bit rates.
  • Spectral texture modeling in the discrete wavelet transform domain is a viable strategy for advanced image compression.
  • This technique offers a robust alternative to traditional modeling methods for image data.