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Proceedings Paper

Image modeling with parametric texture sources for design and analysis of image processing algorithms
Author(s): Chuo-Ling Chang; Bernd Girod
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Paper Abstract

A novel statistical image model is proposed to facilitate the design and analysis of image processing algorithms. A mean-removed image neighborhood is modeled as a scaled segment of a hypothetical texture source, characterized as a 2-D stationary zero-mean unit-variance random field, specified by its autocorrelation function. Assuming that statistically similar image neighborhoods are derived from the same texture source, a clustering algorithm is developed to optimize both the texture sources and the cluster of neighborhoods associated with each texture source. Additionally, a novel parameterization of the texture source autocorrelation function and the corresponding power spectral density is incorporated into the clustering algorithm. The parametric auto-correlation function is anisotropic, suitable for describing directional features such as edges and lines in images. Experimental results demonstrate the application of the proposed model for designing linear predictors and analyzing the performance of wavelet-based image coding methods.

Paper Details

Date Published: 28 January 2008
PDF: 11 pages
Proc. SPIE 6822, Visual Communications and Image Processing 2008, 682229 (28 January 2008); doi: 10.1117/12.769074
Show Author Affiliations
Chuo-Ling Chang, Stanford Univ. (United States)
Bernd Girod, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 6822:
Visual Communications and Image Processing 2008
William A. Pearlman; John W. Woods; Ligang Lu, Editor(s)

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