Edges and gradients in lightness illusions: Role of optical veiling glare.

Lightness Illusions (Contrast, Assimilation, and Natural Scenes with Edges and Gradients) show that appearances do not correlate with the light sent from the scene to the eye. Lightness Illusions begin with a control experiment that includes two identical Gray Regions-Of-Interest(GrayROI) that have equal appearances in uniform surrounds. The Illusion experiment modifies "the-rest-of-the-scene" to make these GrayROIs appear different from each other. Our visual system performs complex-spatial transformations of scene-luminance patterns using two independent spatial mechanisms: optical and neural. First, optical veiling glare transforms scene luminances into a different light pattern on receptors, called retinal contrasts. This article provides a new Python program that calculates retinal contrast. Equal scene luminances become unequal retinal contrasts. Uniform scene segments become nonuniform retinal gradients; darker regions acquire substantial scattered light; and the retinal range-of-light changes. The glare on each receptor is the sum of the individual contributions from every other scene segment. Glare responds to the content of the entire scene. Glare is a scene-dependent optical transformation. Lightness Illusions are intended to demonstrate how our "brain sees" using simple-uniform patterns. However, the after-glare pattern of light on receptors is a morass of high-and low-slope gradients. Quantitative measurements, and pseudocolor renderings are needed to appreciate the magnitude, and spatial patterns of glare. Glare's gradients are invisible when you inspect them. Illusions are generated by neural responses from "the-rest-of-the-scene." The neural network input is the simultaneous array of all receptors' responses. Neural processing performs vision's second scene-dependent spatial transformation. Neural processing generates appearances in Illusions and Natural Scenes. "Glare's Paradox" is that glare adds more re-distributed light to GrayROIs that appear darker, and less light to those that appear lighter. This article describes nine experiments in which neural-spatial-image processing overcompensates the effects of glare. This article studies the first-step in imaging: scene-dependent glare. Despite near invisibility, glare modifies all quantitative measurements of images. This article reveals glare's modification of input data used in quantitative image analysis and models of vision, as well as visual image-quality metrics. Glare redefines the challenges in modeling Lightness Illusions. Neural spatial processing is more powerful than we realized.

Calculating Retinal Contrast from Scene Content: A Program.

This paper describes a computer program for calculating the contrast image on the human retina from an array of scene luminances. We used achromatic transparency targets and measured test target's luminances with meters. We used the CIE standard Glare Spread Function (GSF) to calculate the array of retinal contrast. This paper describes the CIE standard, the calculation and the analysis techniques comparing the calculated retinal image with observer data. The paper also describes in detail the techniques of accurate measurements of HDR scenes, conversion of measurements to input data arrays, calculation of the retinal image, including open source MATLAB code, pseudocolor visualization of HDR images that exceed the range of standard displays, and comparison of observed sensations with retinal stimuli.

Reflectance, illumination, and appearance in color constancy.

We studied color constancy using a pair of identical 3-D Color Mondrian displays. We viewed one 3-D Mondrian in nearly uniform illumination, and the other in directional, nonuniform illumination. We used the three dimensional structures to modulate the light falling on the painted surfaces. The 3-D structures in the displays were a matching set of wooden blocks. Across Mondrian displays, each corresponding facet had the same paint on its surface. We used only 6 chromatic, and 5 achromatic paints applied to 104 block facets. The 3-D blocks add shadows and multiple reflections not found in flat Mondrians. Both 3-D Mondrians were viewed simultaneously, side-by-side. We used two techniques to measure correlation of appearance with surface reflectance. First, observers made magnitude estimates of changes in the appearances of identical reflectances. Second, an author painted a watercolor of the 3-D Mondrians. The watercolor's reflectances quantified the changes in appearances. While constancy generalizations about illumination and reflectance hold for flat Mondrians, they do not for 3-D Mondrians. A constant paint does not exhibit perfect color constancy, but rather shows significant shifts in lightness, hue and chroma in response to the structure in the nonuniform illumination. Color appearance depends on the spatial information in both the illumination and the reflectances of objects. The spatial information of the quanta catch from the array of retinal receptors generates sensations that have variable correlation with surface reflectance. Models of appearance in humans need to calculate the departures from perfect constancy measured here. This article provides a dataset of measurements of color appearances for computational models of sensation.