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Visual psychophysics and color appearance
Published in Sharma Gaurav, Digital Color Imaging Handbook, 2017
Garrett M. Johnson, Mark D. Fairchild
So what is a color appearance model, exactly? The CIE Technical Committee TC1-34, Testing Colour Appearance Models, came up with a definition of what constitutes a color appearance model.59 The definition agreed upon is as follows: “A color appearance model is any model that includes predictors of at least the relative color-appearance attributes of lightness, chroma, and hue.” This is a relatively lenient definition of what constitutes a color appearance model, though it does require some form of a chromatic adaptation transform at the very least. More complicated models are capable of predicting absolute attributes, such as brightness and colorfulness, as well as luminance-dependent effects, such as the Hunt and Stevens effects. Spatially structured phenomena, such as crispening and simultaneous contrast, require both models of spatial vision as well as color appearance.
Fundamentals of Human Vision and Vision Modeling
Published in H.R. Wu, K.R. Rao, Digital Video Image Quality and Perceptual Coding, 2017
Ethan D. Montag, Mark D. Fairchild
Color appearance models [Fai98] attempt to assign values to the color attributes of a sample by taking into account the viewing conditions under which the sample is observed so that colors with corresponding appearance (but different tristimulus values) can be predicted. These models generally consist of a chromatic-adaptation transform that adjusts for the viewing conditions (e.g., illumination, white-point, background, and surround) and calculations of at least the relative color attributes. More complex models include predictors of brightness and colorfulness and may predict color appearance phenomena such as changes in colorfulness and contrast with luminance [Fai98]. Color spaces can then be constructed based on the coordinates of the attributes derived in the model. The CIECAM02 color appearance model [MFH+02] is an example of a color appearance model that predicts the relative and absolute color appearance attributes based on specifying the surround conditions (average, dim, or dark), the luminance of the adapting field, the tristimulus values of the reference white point, and the tristimulus values of the sample.
Comparing Measures of Gamut Area
Published in LEUKOS, 2019
Calculations of gamut area are influenced by the color sample set, model of color perception, and other calculation elements. The magnitude of the differences can be substantial. Color sample sets that utilize too few samples or samples that are not approximately evenly spaced in hue-angle appear to produce results that diverge from other sets in a way that is unrelated to other aspects of color rendition, such as gamut shape. Likewise, color sample sets that include only high-chroma color samples may result in compression of gamut area values for sources that substantially increase gamut area. Gamut area measures that use too many samples can present methodological challenges (related to mismatched test and reference polygons) that have no logical resolution. A reasonable compromise is to use a moderate number of samples (approximately 16) with moderate chroma, either directly or derived using a grouping method—both of these solutions provide similar results. In contrast, differences in the color space lead to systematic differences based on gamut shape. Using a modern color appearance model vetted with color difference data (for example, CAM02-UCS) is advisable.
Tutorial: Background and Guidance for Using the ANSI/IES TM-30 Method for Evaluating Light Source Color Rendition
Published in LEUKOS, 2022
Although some alternative approaches exist, methods for evaluating light source color rendition typically rely on three fundamental components: a standardized model of average human color vision,a set of standardized color samples,and a scheme to define a reference(s) to which calculated colors for a given test light source can be compared. In this way, color rendition can be determined purely mathematically, even if it is a simplification of a more complex issue. In order to establish color rendition as a property of a light source, it must be disassociated from a specific scene and viewer. That is, methods for evaluating light source color rendition predict the influence of a light source without knowledge of the specific objects that will be illuminated or their arrangement. Some known phenomena that influence the appearance of objects, such as the surrounding field, are held constant across calculations so that they are effectively unaccounted for. Likewise, the intensity of the light (that is, the illuminance or luminance) is disregarded in existing methods, although it can strongly influence color appearance (Bao and Wei 2019; Hunt 1952; Kawashima and Ohno 2019; Wei et al. 2020) and has potential to be integrated into specification criteria or more advanced methods. While methods for evaluating light source color rendition play an important role in the lighting industry, it is important to remember that they are not calculations of how a specific room or object will appear, but rather an educated prediction. To understand the color appearance of a specific object in a specific viewing condition, a color appearance model is used, as covered by Fairchild (2013).