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Updated: Jul 7, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Rate-distortion modeling for multiscale binary shape coding based on Markov random fields.

Anthony Vetro1, Yao Wang, Huifang Sun

  • 1Mitsubishi Electr. Res. Labs., Cambridge, MA 07974, USA. avetro@merl.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study explores how Markov random field (MRF) parameters relate to binary shape coding efficiency. Statistical moments from the Chien model predict rate and distortion for multiscale shapes.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Accurate coding of multiscale binary shapes is crucial for efficient data representation.
  • Understanding the relationship between shape characteristics and coding parameters is essential for optimizing compression algorithms.

Purpose of the Study:

  • To investigate the connection between rate-distortion properties of multiscale binary shapes and Markov random field (MRF) parameters.
  • To identify input parameters capable of differentiating shapes across scales and distinguishing between different shapes at a consistent scale.

Main Methods:

  • Utilized the Chien model, an MRF model capturing high-order spatial pixel interactions.
  • Proposed using statistical moments derived from the Chien model as input features.

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  • Employed a neural network to predict rate and distortion based on these statistical moments.

    • Main Results:

    • Demonstrated the capability of statistical moments from the Chien model to serve as effective input for predicting coding performance.
    • Successfully established a predictive relationship between MRF parameters and the rate-distortion characteristics of multiscale binary shapes.

    Conclusions:

    • The proposed method accurately predicts the rate and distortion of binary shapes coded at various scales.
    • Statistical moments of the Chien MRF model offer a robust approach for characterizing multiscale binary shapes for efficient coding.