Illumination and Contrast
Aperture and Object Mode Appearances in Images
Abstract
Vision scientists have segmented appearances into aperture and object modes, based on observations that scene stimuli appear different in a black -no light- surround. This is a 19th century assumption that the stimulus determines the mode, and sensory feedback determines the appearance. Since the 1960’s there have been innumerable experiments on spatial vision following the work of Hubel and Wiesel, Campbell, Gibson, Land and Zeki. The modern view of vision is that appearance is generated by spatial interactions, or contrast. This paper describes experiments that provide a significant increment of new data on the effects of contrast and constancy over a wider range of luminances than previously studied. Matches are not consistent with discounting the illuminant. The observers’ matches fit a simple two-step physical description: The appearance of maxima is dependent on luminance, and less-luminous areas are dependent on spatial contrast. The need to rely on unspecified feedback processes, such as aperture mode and object mode, is no longer necessary. Simple rules of maxima and spatial interactions account for all matches in flat 2D transparent targets, complex 3D reflection prints and HDR displays.
J. J. McCann, “Aperture and Object Mode Appearances in Images”,
in Proc. Electronic Imaging, SPIE vol. 6492-26, 2007.
Proceeding from CIC 2006 - Scottsdale, AZ
Measuring Constancy of Contrast Targets in Different Luminances
Part I – Flat 2-D Displays
This paper combines the flat 2D and 3D experiments described below.
Measuring Constancy of Contrast Targets in Different Luminances
Part I – Flat 2-D Displays
Abstract
This paper measures the departures from perfect constancy with changes in luminance and contrast surrounds. This paper describes an extensive series of experiments studying the appearance of three different contrast targets in different levels of illumination. It significantly extends previous experimental data because of the wide range of contrasts and luminances used. Seven observers matched the same 8 luminance patches in each of three surrounds at five different illumination levels. All matches were made to the same standard. When the illumination was reduced uniformly the results showed a small decrease in matching value with large decreases in illumination level (low-slope behavior). Within a single contrast target the results showed a rapid decrease in matching value with decreasing luminance. The slopes of these data were highest for a white surround, and progressively lower for gray and black surrounds.
Measuring Constancy of Contrast Targets in Different Luminances
Part II – Complex 3-D Scenes
Abstract
This paper measures the departures from perfect constancy with changes in luminance and contrast surrounds. Instead of the 2-D transparent displays used in Part I, these experiments use folded paper targets with printed reflectance patches. This paper targets are illuminated in direct light and shadow. The match results showed the same small decrease in matching value with large decreases in illumination level (low-slope behavior) found in Part I. Within the direct light and in the shade parts of the targets the matches showed the same high-slope contrast behavior. Here, the arrangement of reflectances, illumination and depth from binocular vision did not affect the appearance matches made by observers.
Rendering High-Dynamic Range Images:
Algorithms that mimic Vision
J. J. McCann, “Rendering High-Dynamic Range Images: Algorithms that
Vision”, in Proc. AMOS Technical Conference, US Air Force,Maui, p. 19-28, 2005.
Abstract
Receptors in the human retina respond to a range of light that is 10 billion to 1 in radiance. Yet, the optic nerve has a dynamic range of about 100 : 1. In 1953 Kuffler and Barlow showed that the mammalian optic neurons transmitted information about spatial comparisons. Observers easily discriminate details in scenes with dynamic ranges of 10,000 : 1.
High-dynamic-range Retinex algorithms share a common mechanism with vision, namely they are based on spatial comparison of pixels in all regions of the captured image. These algorithms, now available in commercial products, mimic human image processing. They use multi-resolution spatial comparison techniques to render high-dynamic range scenes.
As shown by Ansel Adams, and Jones & Condit, outdoor scenes typically have dynamic ranges of 1000 : 1 in radiances. Scenes with specular reflections, which contain reflected images of the sun, have much greater ranges. Print paper in actual viewing conditions has a dynamic range of less than 100 to 1. Tone-scale transforms, such as S-shaped H&D curves, cannot render output images to match human sensations. Tone-scale transforms compress the highlights and shadows too much. Spatial-comparison algorithms automatically “dodge and burn” the image based on the spatial content of the input image. They automatically generate the equivalent of scene-dependent spatial-frequency filters.
Many other visual phenomena can be modeled by spatial comparisons. Color constancy, visibility of gradients and edges, appearance of transparency, and color gamut transformations are more closely related than one might think. Experiments have shown that they share a common property, namely they can be explained by human spatial comparison mechanisms.
The Appearance of Brightness and Lightness
J. J. McCann, “ The Appearance of Brightness and Lightness”, in Proc. 8th IS&T/SID Color Imaging Conference, Scottsdale, Arizona, 18-23, 2000.
Abstract
In simplest terms, brightness is the appearance of luminance and lightness is the appearance of objects. The experiments in this paper measure the appearance of three visible faces of a real cube in real-life illumination. Three faces of the cube are painted white and the other three are painted different shades of gray. When the observer sees three white faces the experiment measures the appearance of illumination. When the experimenter rotates the cube to make visible a face with a different reflectance in the same illumination, then the experiment measures the appearance of objects.
The results of matching experiments show that humans make the same match for luminance changes caused by illumination as those caused by reflectance. Humans can successfully recognize changes in whites due to illumination. They mistakenly interpret reflectance changes as illuminant position changes. However, in the same image they make the same matches for dark areas that were caused by illumination, reflectance or both.
