A model of local adaptation

Abstract

Figure 1: Processing steps of our spatial adaptation model. First, optical glare is simulated to produce a retinal image. Then, the local luminance adaptation map is computed using our novel adaptation model. The plots below show the luminance profile for the pixels marked with the dashed-orange line. Note that the eye cannot adapt to small highlights as shown by the flattened blue curve in the "adaptation luminance" plot. As one of the applications, the adaptation map can be used to estimate the smallest visible contrast in complex images (detection map) and therefore represents a visibility tolerance for each pixel. AbstractThe visual system constantly adapts to different luminance levels when viewing natural scenes. The state of visual adaptation is the key parameter in many visual models. While the time-course of such adaptation is well understood, there is little known about the spatial pooling that drives the adaptation signal. In this work we propose a new empirical model of local adaptation, that predicts how the adaptation signal is integrated in the retina. The model is based on psychophysical measurements on a high dynamic range (HDR) display. We employ a novel approach to model discovery, in which the experimental stimuli are optimized to find the most predictive model. The model can be used to predict the steady state of adaptation, but also conservative estimates of the visibility (detection) thresholds in complex images. We demonstrate the utility of the model in several applications, such as perceptual error bounds for physically based rendering, determining the backlight resolution for HDR displays, measuring the maximum visible dynamic range in natural scenes, simulation of afterimages, and gaze-dependent tone mapping.

A model of perceived dynamic range for HDR images

“…In order to develop a generic model that can truthfully predict PDR, sensory and cognitive processes involved in extraction of this attribute from complex stimuli need to be taken into account. Previous research has shown that the area of the brightest patches and the image topology affect the perception of lightness and contrast [17,19,26], and therefore should be considered in constructing such a PDR model. Furthermore, image contrast and colorfulness are important factors in visual perception and, based on previous findings [8,11,24,25], could be involved in the process related to the extraction of observed image attributes.…”

“…In their recent work, Vangorp et al defined a model of local adaptation and use it to measure dynamic range as the ratio between the brightest and darkest image region in which people can still see some details [26]. The model predicts the maximum visible dynamic range for any given scene, based on both glare and local adaptation.…”

A comparative review of tone-mapping algorithms for high dynamic range video

“…While the individual neural units of the HVS are limited to respond in a range of about 1.5 log units [SW12], this capacity is achieved through the utilization of local adaptation mechanisms, such as spatially varying neural gain controls. The local processing makes it possible to restrict adaptation to an area of less than 0.5 visual degrees [VMGM15]. Furthermore, by adapting over time using e.g.…”

The high dynamic range imaging pipeline : Tone-mapping, distribution, and single-exposure reconstruction

“…The response range of the individual neural units is limited to around 1.5 log 10 units [232]. However, adaptation can be restricted to an area of less than 0.5 visual degrees [251], so that the effective dynamic range over the observed scene is larger, around 3.7 log 10 units [141,207]. Moreover, we constantly use saccadic eye movements, and adapt to the lighting close to the focal point both in focus and exposure.…”