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J Anthony Wilson, David H. Brainard; Perceptual evaluation of statistical image models. Journal of Vision 2005;5(12):93. doi: https://doi.org/10.1167/5.12.93.
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© ARVO (1962-2015); The Authors (2016-present)
Introduction: Parametric models of the statistical regularities in natural images may be exploited for image processing and for understanding biological vision. Our current knowledge of how well extant models actually characterize natural images is quite limited. Here we present a psychophysical method that quantifies image model quality and report initial baseline results. Methods: The van Hateren database of natural images was analyzed to determine its pixel intensity histogram. A simple first-order model assumes that pixel intensities are iid and drawn according to this histogram. On each trial of the experiment, the observer viewed two image patches. One patch was extracted from a van Hateren image, while the other was generated from the first-order model. The observer's task was to identify the natural image patch. Patch size was varied parametrically. Data have been collected for two observers. Results: Percent correct increased monotonically with patch size. Threshold (75% correct) patch size was approximately 4 by 4 pixels. Conclusions: Our method quantifies the spatial scale at which failures of the first-order model are detectable by human vision. Similar measurements should provide insight about what aspects of natural images are captured by higher-order statistical image models.
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