Comparison of real colour gamuts using a new reflectance database

Li, Changjun; Luo, Ming Ronnier; Pointer, Michael R.; Green, Phil

doi:10.1002/col.21827

Cited by 18 publications

(7 citation statements)

References 11 publications

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“…Figure shows the CIELAB coordinates for our set of 12 123 samples, assuming the D65 illuminant and CIE 1931 standard observer. As desired, Figure shows a good coverage of the gamut of real objects currently in existence .…”

Section: Experimental Methodsmentioning

confidence: 73%

Assessing the variability of colour‐rendering indices using a random test‐colour method

Fernández

Suero

Pérez

et al. 2017

Coloration Technology

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This paper addresses the way in which the random selection of different sets of samples influences the values of two colour‐rendering indices analogous to the ones proposed by the International Commission on Illumination (CIE Publ. 13.3, 1995), Ra, and the Illuminating Engineering Society (IES Technical Memorandum TM‐30‐15, 2015), Rf. We report results found for 11 light sources, representative of current indoor and outdoor lighting, using a database with 12 123 spectral reflectance factors from real objects. For these 11 light sources, the differences between official Ra and Rf values and those computed using 100 iterations with 6000 random samples from our database proved to be in the range [−13, 2] and [−15, −1], respectively, the highest differences corresponding to sources with narrow peaks in their spectral power distributions. For 100 random subsets of 10 samples from our database, the average standard deviations of the 11 light sources for indices analogous to Ra and Rf were 8 and 5 (or 2 and 1 using 110 samples) respectively. The standard deviations in computed Ra and Rf values, using subsets with different numbers of samples n, can be modelled as inversely proportional to the square root of n. We propose a method to estimate these standard deviations for n = 6000 using reduced sets of 10 spectral reflectance factors.

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Section: Experimental Methodsmentioning

confidence: 73%

Assessing the variability of colour‐rendering indices using a random test‐colour method

Fernández

Suero

Pérez

et al. 2017

Coloration Technology

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“…This spectral‐based colour gamut has been proposed by Li to represent the real surface colours as a part of the work of CIE Technical Committee 1‐73 Real colour Gamut. This gamut was derived from a large database of real‐world object colours . Since it is defined in the spectral reflectance domain, it can be used to derive a gamut for any specific viewing condition.…”

Section: Results and Applicationsmentioning

confidence: 99%

“…This gamut was derived from a large database of real-world object colours. 24 Since it is defined in the spectral reflectance domain, it can be used to derive a gamut for any specific viewing condition.…”

Section: Standard Colour Gamutsmentioning

confidence: 99%

Metrics for comparing and analyzing two colour gamuts

Deshpande

Green

Pointer

2014

Color Research & Application

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In printing industry, it is often necessary to be able to compare two or more colour gamuts, to determine how similar they are. For example, if a colour image is to be retargeted from one output medium to a different medium, we may wish to know whether the second has a sufficiently similar gamut to make an acceptable reproduction a possibility. The gamut volume alone enables a comparison of the size of the gamut, but not whether the gamuts intersect sufficiently to meet the reproduction aims. This can be achieved by visual comparison of the two gamut volumes in a pseudo-3D rendering, but it is also useful to have a single-number value, which enables this comparison to be computed from the gamut boundary description of the two gamuts. CIE TC8-05 defined metrics for evaluating single colour gamuts and these metrics can be used to make some comparisons between gamuts. However, they do not give information on how well one gamut can match another in terms of both intersection and volume. In this work, a number of metrics are proposed for comparing and analyzing two colour gamuts. The primary metric is the Gamut Comparison Index (GCI), which quantifies the similarity between gamuts. A number of other related metrics are also defined, and several test cases for the metrics are computed. The results indicate that the metrics give a consistent prediction of how well the two gamuts compared. Use of the resulting GCI value should enable users to select the transform or device that best matches the reproduction aim. It should also help vendors and users evaluate the gamuts of their devices against the appropriate standard colour gamuts.

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“…As noted before, the q values correspond to the postadaptation values R c , G c , and B c (or R wc , G wc , and B wc ) in the cone‐like space in CAM16. Thus, we investigated the range of q values in real situations, using a large reflectance data set with approximately 65900 samples, accumulated at the University of Leeds and used for the comparison of surface color gamuts, and a data set of illuminants used to study the color rendition of light sources/illuminants . This light source/illuminant data set has 401 spectral power distributions (SPDs), including D‐series illuminants, blackbody radiation, tungsten filament, high‐intensity discharge lamps, broadband and narrowband fluorescent lamps, and mixed and phosphor Light‐emitting diodes (LEDs).…”

Section: Estimating the Range Of Q Values For Real Object Colorsmentioning

confidence: 99%

Further investigation on the modified hyperbolic function in the CAM16 color appearance model

Wang

Gao

Yang

et al. 2019

Color Research & Application

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In the International Commission on Illumination (CIE) color appearance model CIECAM02, a modified hyperbolic function is used to represent luminance adaptation. The same nonlinear function is also used in the new color appearance model CAM16 [Color Res Appl., 2017;42:703‐718]. Although the modified hyperbolic function seems reasonable based on physiological evidence, it has an infinite slope at the origin, which causes instability for both the forward and inverse modes of the CIECAM02/CAM16 models. In this article, various possible extensions to the nonlinear luminance adaptation function in CIECAM02/CAM16 are reviewed and evaluated. Based on these investigations, the Gill extension to the hyperbolic function that is used to represent luminance adaptation [Proceedings of 16th Color and Imaging Conference, pp. 327‐331, 2008], is recommended at both the lower end (q < qL) and the upper end (q > qU), where q is the appropriate Rc, Gc, Bc (or Rwc, Gwc, Bwc) response. In addition, the new recommended function can be readily inverted for use in the appropriate inverse appearance model. From an extensive analysis using available experimental data sets, we also propose that, for the lower and upper limits of the luminance range in the extended model, the values qL = 0.26 and qU = 150 be used, respectively.

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Comparison of real colour gamuts using a new reflectance database

Cited by 18 publications

References 11 publications

Assessing the variability of colour‐rendering indices using a random test‐colour method

Assessing the variability of colour‐rendering indices using a random test‐colour method

Metrics for comparing and analyzing two colour gamuts

Further investigation on the modified hyperbolic function in the CAM16 color appearance model

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