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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Image compression via joint statistical characterization in the wavelet domain.

R W Buccigrossi1, E P Simoncelli

  • 1Turner Consulting Group, Washington, DC 20008, USA. butch@tcg-inc.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel probability model for natural images based on wavelet transform statistics. The developed image coder (EPWIC) demonstrates competitive rate-distortion performance, highlighting the model's effectiveness.

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Area of Science:

  • Image processing and computer vision
  • Statistical modeling and machine learning
  • Signal processing and data compression

Background:

  • Natural images exhibit complex statistical properties in the wavelet transform domain.
  • Existing models often fail to capture the intricate dependencies between wavelet coefficients.
  • Understanding these statistics is crucial for developing efficient image compression techniques.

Purpose of the Study:

  • To develop a probability model for natural images based on wavelet coefficient statistics.
  • To explain the observed non-Gaussian marginal and joint properties of wavelet coefficients.
  • To construct an image coder demonstrating the practical application and effectiveness of the proposed model.

Main Methods:

  • Empirical observation of statistics in the wavelet transform domain for various image types.
  • Development of a Markov model incorporating linear prediction, multiplicative, and additive uncertainties.
  • Construction of the Embedded Predictive Wavelet Image Coder (EPWIC) using the model for conditional probabilities and a greedy bitplane ordering algorithm.

Main Results:

  • Identified non-Gaussian marginals (heavy-tailed) and correlated magnitudes of adjacent wavelet coefficients.
  • The proposed Markov model accurately accounts for the statistical properties of diverse image types.
  • The EPWIC coder achieved rate-distortion performance comparable to state-of-the-art image coders.

Conclusions:

  • The developed probability model effectively captures the statistical regularities of natural images in the wavelet domain.
  • The EPWIC demonstrates the practical utility of the model for image compression.
  • The model's simplicity belies its power in achieving competitive compression performance.