Visual discomfort and the spatial distribution of Fourier energy

•A computational measure for visual discomfort from images is proposed.•We measured the Fourier amplitude spectrum of images in two dimensions.•Departure from 1/f in the 2D domain predicted discomfort.•The measure may help designers identify and avoid visually aversive designs. Quite independently of what they represent, some images provoke discomfort, and even headaches and seizures in susceptible individuals. The visual system has adapted to efficiently process the images it typically experiences, and in nature these images are usually scale-invariant. In this work, we sought to characterize the images responsible for discomfort in terms of their adherence to low-level statistical properties typically seen in natural scenes. It has been conventional to measure scale invariance in terms of the one-dimensional Fourier amplitude spectrum, by averaging amplitude over orientations in the Fourier domain. However, this loses information on the evenness with which information at various orientations is represented. We therefore fitted a two-dimensional surface (regular circular cone 1/f in logarithmic coordinates) to the two-dimensional amplitude spectrum. The extent to which the cone fitted the spectrum explained an average of 18% of the variance in judgments of discomfort from images including rural and urban scenes, works of non-representational art, images of buildings and animals, and images generated from randomly disposed discs of varying contrast and size. Weighting the spectrum prior to fitting the surface to allow for the spatial frequency tuning of contrast sensitivity explained an average of 27% of the variance. Adjusting the shape of the cone to take account of the generally greater energy in horizontal and vertical orientations improved the fit, but only slightly. Taken together, our findings show that a simple measure based on first principles of efficient coding and human visual sensitivity explained more variance than previously published algorithms. The algorithm has a low computational cost and we show that it can identify the images involved in cases that have reached the media because of complaints. We offer the algorithm as a tool for designers rather than as a simulation of the biological processes involved.