Image modeling with parametric texture sources for design and analysis of image processing algorithms

Chuo-Ling Chang; Bernd Girod

doi:10.1117/12.769074

28 January 2008 Image modeling with parametric texture sources for design and analysis of image processing algorithms

Chuo-Ling Chang, Bernd Girod

Proceedings Volume 6822, Visual Communications and Image Processing 2008; 682229 (2008) https://doi.org/10.1117/12.769074
Event: Electronic Imaging, 2008, San Jose, California, United States

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.

Citation Download Citation

Chuo-Ling Chang and Bernd Girod "Image modeling with parametric texture sources for design and analysis of image processing algorithms", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 682229 (28 January 2008); https://doi.org/10.1117/12.769074

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