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Natural image statistics as a function of dynamic range

Publication by Antoine Grimaldi, David Kane, Marcelo Bertalmío
Related to the Smart Asset re-Use in Creative Environments (SAUCE) project
Published in Journal of Vision, 2018


The statistics of real world images have been extensively investigated, in virtually all cases using low dynamic range (LDR) image databases. The few studies that have considered high dynamic range (HDR) images have performed statistical analysis over illumination maps with HDR from different sets (Dror et al. 2001) or have examined the difference between images captured with HDR techniques against those taken with single-exposure LDR photography (Pouli et al. 2010). In contrast, in this study we investigate the impact of dynamic range upon the statistics of equally created natural images. To do so we consider the HDR database SYNS (Adams et al. 2016). For the distribution of intensity, we observe that the standard deviation of the luminance histograms increases noticeably with dynamic range. Concerning the power spectrum and in accordance with previous findings (Dror et al. 2001), we observe that as the dynamic range increases the 1/f power law rule becomes substantially inaccurate, meaning that HDR images are not scale invariant. We show that a second-order polynomial model is a better fit than a linear model for the power spectrum in log-log axis. A model of the point-spread function of the eye (considering light scattering, pupil size, etc.) has been applied to the datasets creating a reduction of the dynamic range, but the statistical differences between HDR and LDR images persist and further study needs to be performed on this subject. Future avenues of research include utilizing computer generated images, with access to the exact reflectance and illumination distributions and the possibility to generate very large databases with ease, that will help performing more significant statistical analysis.