Poster Session: Perceptual scaling and natural image statistics.

Abstract

Visual coding is thought to be matched to many of the properties of the natural visual environment, such as the characteristic amplitude spectra or fractal geometry of natural scenes. This match has been probed in a wide variety of ways. Here we used the paradigm of "maximum likelihood difference scaling" (MLDS, Maloney and Knoblauch Ann Rev Vis Sci 2020) to explore the perceptual representation of the spatial structure of images. The MLDS task involves presenting pairs of images drawn from different levels along a dimension and judging which pair has greater difference. In our case the stimulus dimension corresponded to the slope of the image amplitude spectrum. Grayscale noise images were filtered to form a range of slopes from 0 to -2 in steps of 0.2. Further image arrays were generated by first binarizing the image intensities and then extracting only the edges to isolate the fractal structure (corresponding to fractal dimension range from 1 to 2 in steps of 0.1). For each array the MLDS task was used to estimate the perceived differences. Scaling for fractal dimension did not differentially favor natural fractal values. However for the amplitude spectra, the derived perceptual scales tended to be steeper for intermediate levels of the array and shallower for both strongly blurred or sharpened levels, consistent with greater perceptual salience for image differences that have more naturalistic spectra.