G0 Revisited as Equally Bright Reference Boundary

2021 ◽  
Vol 2021 (29) ◽  
pp. 247-252
Author(s):  
Hao Xie ◽  
Mark D. Fairchild
Keyword(s):  
Color Appearance ◽  
Color Patch ◽  
Paired Comparisons ◽  
Chromaticity Diagram ◽  
Color Science ◽  
Appearance Models ◽  
Absolute Brightness

Brilliance and zero grayness (denoted as G0) and are two terms coined by Ralph Evans. Nayatani, Heckaman and Fairchild have done series of work to incorporate them into comprehensive color appearance models. In this work, those concepts were reexamined to scale lightness/brightness across the chromaticity diagram. Specifically, observers, mostly with a color science background, were asked to adjust the luminance of a color patch to appear with no grayness, or equivalently just about/cease to glow. The hypothesis was that lightness can be equalized across those chromaticities and the Helmholtz-Kohlrausch effect is automatically incorporated. This hypothesis was verified in a follow-up experiment where another group of observers completed paired comparisons of the brightness between the collected G0 results. The G0 task was also repeated under another two levels of adaption backgrounds, based on which different absolute brightness results for a given chromaticity might be derived. In addition, high correlations between the G0 results (as a perceptual boundary between appearance modes) and different physical gamut boundaries including MacAdam's optimal colors were found for possible computational proxies and ecologically meaningful implications.

Change of color appearance due to extremely high light level: corresponding colors under 100 and 3000 cd/m2

2019 ◽  
Vol 2019 (1) ◽  
pp. 320-325 ◽  
Author(s):  
Wenyu Bao ◽  
Minchen Wei
Keyword(s):  
Light Level ◽  
Limited Range ◽  
High Light ◽  
Color Preference ◽  
Color Appearance ◽  
Appearance Model ◽  
Color Matching ◽  
Light Levels ◽  
Appearance Models ◽  
Psychophysical Study

Great efforts have been made to develop color appearance models to predict color appearance of stimuli under various viewing conditions. CIECAM02, the most widely used color appearance model, and many other color appearance models were all developed based on corresponding color datasets, including LUTCHI data. Though the effect of adapting light level on color appearance, which is known as "Hunt Effect", is well known, most of the corresponding color datasets were collected within a limited range of light levels (i.e., below 700 cd/m2), which was much lower than that under daylight. A recent study investigating color preference of an artwork under various light levels from 20 to 15000 lx suggested that the existing color appearance models may not accurately characterize the color appearance of stimuli under extremely high light levels, based on the assumption that the same preference judgements were due to the same color appearance. This article reports a psychophysical study, which was designed to directly collect corresponding colors under two light levels— 100 and 3000 cd/m2 (i.e., ≈ 314 and 9420 lx). Human observers completed haploscopic color matching for four color stimuli (i.e., red, green, blue, and yellow) under the two light levels at 2700 or 6500 K. Though the Hunt Effect was supported by the results, CIECAM02 was found to have large errors under the extremely high light levels, especially when the CCT was low.

A Digital Test Chart for Visual Assessment of Color Appearance Scales

2021 ◽  
Vol 2021 (29) ◽  
pp. 160-165
Author(s):  
Mark D. Fairchild
Keyword(s):  
Human Perception ◽  
Visual Assessment ◽  
Model Testing ◽  
Color Appearance ◽  
Psychophysical Data ◽  
Appearance Models ◽  
Digital Test ◽  
Computational Comparison

A digital color appearance test chart, akin to a ColorChecker® Chart for human perception, was developed and evaluated both perceptually and computationally. The chart allows an observer to adjust the appearance of a limited number of color patches to allow a quick evaluation of perceived brightness, colorfulness, lightness, saturation, and hue on a display. The resulting data can then be used to compared observed results with the predictions of various color appearance models. Analyses in this paper highlight some known shortcomings of CIELAB, CIECAM02, and CAM16. Differences between CIECAM02 and CAM16 are also highlighted. This paper does not provide new psychophysical data for model testing, it simply describes a technique to generate such data and a computational comparison of models.

The color appearance shifts of woven fabrics induced by the optical blending of colored yarns

2019 ◽  
Vol 90 (3-4) ◽  
pp. 395-409
Author(s):  
Youngjoo Chae
Keyword(s):  
Woven Fabrics ◽  
Color Images ◽  
Color Appearance ◽  
Wide Range ◽  
Cathode Ray Tube ◽  
Appearance Models ◽  
Scanned Images ◽  
Color Mixing ◽  
The Individual ◽  
Optical Color

This study investigated the effect of individual yarn colors and their blending on the color appearance of woven fabrics by comparing them with solid colors. Woven fabrics often obtain their colors through blends of different colored yarns. When the blends are seen from far enough away, the individual yarn colors are optically mixed in our eyes and perceived as a new solid color that is not actually present. To examine this optical color mixing effect, red, yellow, green, and blue yarns were woven together to produce 36 fabrics in a wide range of colors, the values of which were measured spectrophotometrically. The spectrophotometric values were generated as solid color images on a calibrated cathode ray tube (CRT) monitor. Then the fabrics were scanned and the scanned images were displayed beside their corresponding solid color images on the CRT monitor to assess their differences in lightness, colorfulness, and hue. The results showed that, although the fabrics and their corresponding solid colors had identical CIELAB color values, they appeared significantly different in terms of all lightness, colorfulness, and hue. It was found that the lightness differences of fabrics from solid colors vary with the overall L*, C*, and h° of the fabrics, the colorfulness differences vary with the L*, C*, and h°, and the number of yarn colors in the fabrics and the hue differences vary with the h°. Based on these effects, color appearance models to predict the perceived lightness, colorfulness, and hue of woven fabrics were developed.

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