Closed-Form Correlation Model of Oriented Bandpass Natural Images

Most prevalent statistical models of natural images characterize only the univariate distributions of divisively normalized bandpass responses or wavelet-like decompositions of them. However, the higher-order dependencies between spatially neighboring responses are not yet well understood. Towards f...

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Bibliographic Details
Published inIEEE signal processing letters Vol. 22; no. 1; pp. 21 - 25
Main Authors Che-Chun Su, Cormack, Lawrence K., Bovik, Alan C.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Bandpass
Bivariate model
closed-form
Computational modeling
Computer programs
Correlation
Decomposition
Exact solutions
Gaussian distribution
Hidden Markov models
Histograms
Joints
Mathematical analysis
natural scene statistics (NSS)
Robustness
spatial-oriented correlation model
Statistical analysis
Wavelet transforms
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Summary:Most prevalent statistical models of natural images characterize only the univariate distributions of divisively normalized bandpass responses or wavelet-like decompositions of them. However, the higher-order dependencies between spatially neighboring responses are not yet well understood. Towards filling this gap, we propose a new closed-form spatial-oriented correlation model that captures statistical regularities between perceptually decomposed natural image luminance samples. We validate the new correlation model on a variety of natural images. Experimental results demonstrate the robustness of the new correlation model across image content. A software release that implements the new closed-form spatial-oriented correlation model is available at http://live.ece.utexas.edu/research/3dnss/bicorr_release.zip.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2345765