Statistics of natural images as a function of dynamic range

Abstract

The statistics of real world images have been extensively investigated, but in virtually all cases using only low dynamic range image databases. The few studies that have considered high dynamic range (HDR) images have performed statistical analyses categorizing images as HDR according to their creation technique, and not to the actual dynamic range of the underlying scene. In this study we demonstrate, using a recent HDR dataset of natural images, that the statistics of the image as received at the camera sensor change dramatically with dynamic range, with particularly strong correlations with dynamic range being observed for the median, standard deviation, skewness, and kurtosis, while the one over frequency relationship for the power spectrum breaks down for images with a very high dynamic range, in practice making HDR images not scale invariant. Effects are also noted in the derivative statistics, the single pixel histograms, and the Haar wavelet analysis. However, we also show that after some basic early transforms occurring within the eye (light scatter, nonlinear photoreceptor response, center-surround modulation) the statistics of the resulting images become virtually independent from the dynamic range, which would allow them to be processed more efficiently by the human visual system.

Vision Models for Wide Color Gamut Imaging in Cinema

“…This assumption would correspond (going back to the vision fundamentals mentioned in Section 3) to having as input image for our GM framework the signal generated by the photoreceptors. The reason for this is the well known fact that the Naka-Rushton equation that models photoreceptor responses optimizes the performance efficiency of cones and rods by adapting the possibly high dynamic range (HDR) input intensities to the SDR representation capabilities of photoreceptors [70]. In fact several successful tone mapping approaches (that convert HDR images into SDR) in the computer graphics and image processing communities use non-linear curves based on the Naka-Rushton model (e.g.…”

Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies

“…Once more we have an element that corroborates the efficient coding theory, which is the following. The average luminance histogram of natural images is, in log-log coordinates, piece-wise linear [25], with two lines of different slope at each side of the average level, see Fig. 4 (left).…”

“…Natural image statistics do change with the dynamic range of the scene, but basic retinal transforms (like photoreceptor response and center-surround inhibition at the level of retinal ganglion cells) have been shown to make the signal statistics almost fully independent of the dynamic range [25]. Indeed, most of the vision literature on dynamic range focused on how objects appear invariant over changing lighting conditions, thus omitting irradiance information [45].…”

“…We estimate the average μ of the light intensity based on the median of the luminance channel Y of the HDR source; the median provides a better fit than the mean to the piecewise linear shape, in log-log coordinates, of the average histogram of natural scenes [25]. The values for γ + , γ − are obtained as the slopes of the piece-wise linear fit to the cumulative histogram of the luminance.…”

Perceived Color Gamut in Images: From Boundary to Difference

“…It has been shown that natural image statistics correlate with perceptual sensitivity and preference (McDermott and Webster, 2012 ; Nascimento et al, 2021 ). Using natural image datasets, the distribution of dynamic range has been characterized (Grimaldi et al, 2019 ), however, the generalization to the chromatic dimension has not been carried out. Moreover, color reproduction does not only concern natural objects, as synthetic scenes generated by computer graphics may also benefit from a larger CGV, although the perceptual gain is not easily quantifiable.…”