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Three new approximately uniform color spaces named MLUV, MLUV1, and MLUV2 were developed by modifying CIELUV lightness, u′v′ chromaticity coordinates, and u*v* color coordinates, respectively. Performance tests using the combined and four individual datasets employed at CIEDE2000 development showed that MLUV, MLUV1 and MLUV2 were significantly better than CIELUV. Using values of Standardized Residual Sum of Squares (CIE 217:2016) for predictions of the four individual datasets the ranking (from best to worst) was MLUV, MLUV2 and MLUV1, but for predictions of two ellipses datasets the ranking (from best to worst) was MLUV2, MLUV, and MLUV1. Overall, the MLUV2 space was found to be the best. Hence, it is expected that MLUV2 could be used for color specification and color difference evaluations, especially in industrial applications that depend on additive light mixing, such as color TV sets, video monitors, and lighting. [ABSTRACT FROM AUTHOR]

The paper describes a comprehensive test to evaluate the performance of current colour‐difference models using available experimental datasets. In total, 28 individual datasets were accumulated to test 17 colour‐difference formulae, 13 of them based on Uniform Colour Spaces (UCSs) in terms of the Standardized Residual Sum of Squares (STRESS) measure. The 28 datasets were divided into three groups: Large Colour‐Difference data (LCD), Small Colour‐Difference data for surface colours (SCDs), and Small Colour Difference data for display colours (SCDd). For each colour model, four versions were tested: the original model, and that including kL‐, Gamma‐ and kL/Gamma, which are the lightness parametric factor, the colour‐difference exponent factor, and the combination of both, respectively, optimized to fit particular dataset(s). The statistical F‐test was applied to test the difference between each pair of models. Furthermore, parametric effects between the large/small colour‐difference magnitudes, and between surface/display colours were investigated. The results showed that CAM16‐UCS significantly outperformed the other models for all groups. It accurately predicted all types of data and should be proposed for colour‐difference evaluation across all industries. [ABSTRACT FROM AUTHOR]

We have described a number of transformations by mapping the cone signal vector C, formed by LMS, to opponent space O, formed by achromatic signal OA, red‐green signal Org, and yellow‐blue signal Oyb. Two new transformations, Γop1 and Γop2, are proposed, based on the CIE 2006 spectral luminous efficiency function, VFλ, and Hurvich's opponent spectral red‐green and yellow‐blue responses, Hrgλ and Hybλ, without and with constraints, respectively. Γop1 is better than Γop2 in terms of best fit of Hrgλ and Hybλ, and both transformations improved previously proposed ones. Finally, we discuss the neutral conditions in cone space using each of the new and some of the extant transformations, as well as choosing scaling factors so that the neutral conditions in cone space are satisfied. [ABSTRACT FROM AUTHOR]

A study by Gao et al found that the Newton method may not converge for certain combinations of the photopic luminance and the ratio of scotopic and photopic luminance for computing the adaptation coefficient m, for the CIE MES2 system for mesopic photometry. Hence, they proposed to use the Bisection‐Newton method. In this short note, we propose the Newton method with a new initial guess for computing m. Numerical simulation has shown the proposed method not only converges, but also converges faster than the Bisection‐Newton method. [ABSTRACT FROM AUTHOR]

We studied skin color in participants before and after a 100‐m sprint using a Konica Minolta CM‐2600d spectrophotometer. Four body positions (forehead, cheek, neck, and inner forearm) were measured for the approximately 30 students (non‐professional runners) who participated in the experiment. Skin reflectance was measured three times at each position before running and again soon afterward. Analysis of the data showed that average measurement repeatability (mean color difference from the mean [MCDM]) was 0.5, 0.4, 0.3, and 0.2 CIELAB units for forehead, cheek, neck, and inner forearm, respectively. However, average skin color differences produced by running were 1.8, 2.2, 2.0, and 1.7 CIELAB units for forehead, cheek, neck, and inner forearm positions, respectively, which are considerably higher than repeatability before or after running. Furthermore, appearance variation was analyzed in the CIELAB space, and it was found that sprinting for 100 m considerably changes the values of the red‐green a* coordinate, while the lightness L* and yellow‐blue b* coordinates remain almost constant. More specifically, on average, a* decreases after running for the forehead, cheek, and neck positions, while the opposite is true for the inner forearm position. The findings in this article may be useful to test previous physical models, achieve more realistic images of human skin after sprinting, and contribute to work of CIE TC 1‐92 on skin spectra database. [ABSTRACT FROM AUTHOR]

Experience and experimental results show that the perceived colour appearance of an object varies with the size of that object or the viewing angle. Based on available visual size‐effect data, it is found that the size effect can be compensated in the CAT16 cone‐like space. Furthermore, it is found that, similar to the von Kries chromatic adaptation, size‐effect correction can be operated independently on the different cone channels. Hence, the size effect can be easily built into the CAM16 model to predict the colour appearance for stimuli of varying sizes. [ABSTRACT FROM AUTHOR]

A comprehensive color appearance model should be capable of predicting color appearance under a wide range of viewing conditions, including related and unrelated colors. The current CAM16 model was designed to predict the appearance of related colors and is in the process of becoming a CIE recommendation, to replace the current CIECAM02 model. This article describes an extension of CAM16 (named CAM20u) for predicting the brightness, hue quadrature, amount‐of‐white, and colorfulness attributes of unrelated colors. Its performance, together with that of other unrelated models, was tested based on the three available datasets. The results show that the CAM20u model significantly outperforms the others, especially for colorfulness and amount‐of‐white. [ABSTRACT FROM AUTHOR]

Research suggested that color vision evolved for the purpose of seeing skin color modulations signaling emotion or mood. Hence people want to find the connection of the cone sensitivities with the skin spectral reflectance. In this article the W and V shapes based on skin spectral reflectance measured at different positions and from different ethnic groups (Kurdish, Caucasian, Thai, Chinese) were investigated. First, it was found that the measured skin spectral reflectance from all body positions and ethnic groups have a V shape between 400 and 450 nm and a W shape between 520 and 610 nm. The W and V shapes from the measured skin spectral reflectance are clearly different from those from the spectral reflectance generated from the physical model. The V shape and W shape do not exist with spectral reflectances measured from artificial skin color. Second, it was found that the wavelengths where the left trough assumes for the W shape are different from the wavelength where the M cone peaks, and the wavelengths where the central peak of the W shape assumes are different from the wavelength where the L cone peaks, which are different from the results from literature. Third, it was found that the skin spectral reflectance varies with body positions and ethnic groups. The W shapes turn to be a “U” shape for the spectral reflectance measured at back of the hand and inner forearm positions. The spectral reflectance measured from Kurdish starts decreasing for each body positions for wavelength greater than 700 nm, which is clearly different from the spectral reflectance measured from the Chinese and Caucasian ethnic groups. The findings of this paper are valuable for improving the physical model for generating skin spectral reflectance, reconstructing skin spectral reflectance based on image, identifying human skin color from spectral imaging and rendering of human skin color in computer graphics. [ABSTRACT FROM AUTHOR]

Recently, Cao et al proposed an adaptive weighting method for the training samples for reflectance reconstruction according to both colorimetric and spectral reflectance similarities for a given vector defined by tristimulus values. It was shown the Cao et al method outperforms the other methods including the regression estimation method in terms of multiple evaluation criteria. In this article, motivated by the work of Cao et al, a hybrid weight is introduced, which results in the size of the training samples selected is half of that used by the Cao et al method. Simulation results showed that the proposed method performs equally well as or slightly better than the Cao et al method, but uses less central processing unit time than that used by the Cao et al method. It was also found that about 100 training samples selected is good enough for the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Color‐appearance models, CIECAM02 and CAM16, usually include two one‐step chromatic adaptation transforms: a forward (one‐step) transform to convert data from a first illuminant to CIE illuminant E, plus a reverse (one‐step) transform to convert the results from CIE illuminant E to a second illuminant. In practice, however, one‐step chromatic adaptation models, that avoid the use of the intermediate CIE illuminant E, are also employed. Tests using the one‐step CAT16 model indicate failures of both the symmetry and transitivity properties, except in the case where the degree of adaptation D is equal to unity. The magnitude of these failures depends on the specific illuminants selected, and increases as the degree of adaptation decreases. From four possible two‐step CAT16 models, we have identified two that obey the symmetry and transitivity properties, one with slightly better predictions of the experimental corresponding‐color datasets available in the literature, and more consistent with the one‐step CAT16 model. The findings of this article confirm that, for incomplete adaptation, the use of the one‐step CAT is incorrect, and we propose that the use of a two‐step CAT16 model be mandatory for future applications. [ABSTRACT FROM AUTHOR]

The CIECAM02 color-appearance model enjoys popularity in scientific research and industrial applications since it was recommended by the CIE in 2002. However, it has been found that computational failures can occur in certain cases such as during the image processing of cross-media color reproduction applications. Some proposals have been developed to repair the CIECAM02 model. However, all the proposals developed have the same structure as the original CIECAM02 model and solve the problems concerned at the expense of losing accuracy of predicted visual data compared with the original model. In this article, the structure of the CIECAM02 model is changed and the color and luminance adaptations to the illuminant are completed in the same space rather than in two different spaces, as in the original CIECAM02 model. It has been found that the new model (named CAM16) not only overcomes the previous problems, but also the performance in predicting the visual results is as good as if not better than that of the original CIECAM02 model. Furthermore the new CAM16 model is simpler than the original CIECAM02 model. In addition, if considering only chromatic adaptation, a new transformation, CAT16, is proposed to replace the previous CAT02 transformation. Finally, the new CAM16-UCS uniform color space is proposed to replace the previous CAM02-UCS space. A new complete solution for color-appearance prediction and color-difference evaluation can now be offered. [ABSTRACT FROM AUTHOR]

This article addresses some general practices for the computation of CIE tristimulus values (TSVs), including different methods used for the interpolation, extrapolation, and truncation of data in the visible wavelength range. In each case, a quantitative analysis is presented on the basis of a dataset with 2365 spectral reflectances covering a wide color gamut, assuming six CIE illuminants and the two CIE standard colorimetric observers. For interpolation, it is found that among five tested methods, the (cubic) spline method is the best one when the spectral reflectance factors are uniformly sampled over the visible wavelength range. For extrapolation, it is found that the second-order extrapolation method gives better results than the use of nearest available data points or linear extrapolation methods. With respect to truncation, considering five different usual truncated ranges, it is found that the range from 360 to 780 nm provides more accurate results than the range from 380 to 780 nm currently recommended by CIE for nonfluorescent samples, and the use of the truncated range from 400 to 700 nm in place of the CIE-recommended range of 360 to 830 nm leads to TSVs with considerably high color differences of up to 0.9 CIELAB units. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 10-18, 2017 [ABSTRACT FROM AUTHOR]

The least squares method for computing colorimetric weighting tables is presented and its connection with the optimum weights method is investigated. Each requires solving three linear systems of equations with the same coefficient matrix but three different right hand side vectors. It is shown that the two methods have nearly the same performance when the wavelength interval of the data is large. The two methods however, will perform differently when Δ λ is small. Comparisons are also made between the least squares method, the optimum weights method, the zero- and second-order weighting tables, and the ASTM weighting tables, both the original 1985 tables and the new 2013 Tables V and VI. The results show that the least squares method is the best for measurement intervals equal to or lower than 10 nm, and is competitive with the optimum weights method for 20 nm steps. The results presented in this article will contribute to the work of CIE Technical Committee TC1-71 Tristimulus Integration as it seeks to make recommendations for the calculation of tristimulus values. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 125-142, 2016 [ABSTRACT FROM AUTHOR]

Brill [Color Res Appl 2006;31:142-145] and Brill and Süsstrunk [Color Res Appl 2008;33:424-426] found that CIECAM02 has the yellow-blue and purple problems and gave partial solutions to them. In this article we model the optimum solution to the yellow-blue and purple problems simultaneously as a constrained non-linear optimization problem. An optimum solution resulting in a new CAT02 matrix is numerically obtained. This new matrix satisfies the nesting rule and performs better than the Hunt-Pointer-Estévez (HPE) matrix in predicting both corresponding colours and colour appearance data sets. Specifically, it was found that the new and HPE matrices performed significantly different on nine (out of 21) corresponding colour data sets and on all corresponding colours data sets as a whole. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 491-503, 2015 [ABSTRACT FROM AUTHOR]

Here, we investigated the CAT02 and HPE triangles in both x,y and u′, v′ spaces. It was found that in u′, v′ space, the HPE triangle is separated into two regions by the line 3u′ − 8v′ + 6 = 0, which is the u′, v′ image of X + Y + Z = 0 under the mapping from ( X, Y, Z) to ( u′, v′). One of these two regions admits all-positive R,G,B triplets and the other all-negative R,G,B triplets, under the case of ( X + Y + Z) > 0. The findings of this study explained the computation problems of CAT02 and provided methods for solving the Yellow-Blue and Purple problems simultaneously. © 2013 Wiley Periodicals, Inc. Col Res Appl, 40, 30-39, 2015 [ABSTRACT FROM AUTHOR]

A large set of data, comprising the spectral reflectances of real surface colours, has been accumulated. The data comprise 16 groups with different materials and include 85,879 measured spectra. From these data, CIELAB colorimetric coordinates were calculated under CIE illuminant D50 and the CIE 1931 standard colorimetric (2°) observer. Several published colour gamuts including those developed by Pointer and ISO reference colour gamut [ISO Graphic Technology Standard 12640-3:2007] were compared using the present data set. It was found that the Pointer gamut is smaller than the new real data in most of the colour regions. The results also showed that the ISO reference colour gamut is larger than the new real accumulated data in most regions. The present finding indicates that there is a need to derive a new colour gamut based on the newly accumulated data for common applications. [ABSTRACT FROM AUTHOR]

CIECAM02 has been used to predict colour appearance under a wide range of viewing conditions, to provide a uniform colour space, and to provide a profile connection space for colour management. However, some problems have been identified with CIECAM02. CIE Technical Committee 8-11 is currently working on the methods to resolve the problems for practical applications. Part of the CIE TC8-11 work is to repair the lightness computational failure. This article incorporates most of the previous suggestions and makes some further tests. It is hoped that this article will aid the ongoing work of the CIE TC8-11 and perhaps be the starting point for repairing the CIECAM02 for colour management and for the new recommendation. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 143-153, 2014 [ABSTRACT FROM AUTHOR]

A set of 10 color pairs was proposed and produced in 2002 to show the advantages of the CIEDE2000 color-difference formula with respect to CIELAB. These 10 color pairs illustrated each of the five corrections to CIELAB proposed by CIEDE2000. The 10 color pairs were visually assessed, under reference conditions close to those proposed by CIEDE2000, by two groups of 31 and 21 inexperienced observers, using two different gray scales. Average visual results in these experiments fitted CIEDE2000 predictions much better than CIELAB, as shown by a decrease of Standardized Residual Sum of Squares values of about 20 units. Current visual results showed only the improvement of CIEDE2000 upon CIELAB in predictions of perceived color differences, but they are not recommended for testing new advanced color-difference formulas. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 429-436, 2013. [ABSTRACT FROM AUTHOR]

Liu et al. [Color Res Appl 1995;20:245-250] compared the CIELAB hue angles under CIE illuminants D65 and A for quantifying the color appearance changes for gem materials. They found that CIELAB hue angle for some gem materials under illuminant D65 was larger than under A, which is contrary to the perceived blue and purple appearances under daylight and incandescent light sources, respectively. They called this phenomenon as the abnormal hue angle change in the CIELAB space for the gem materials. In this article, we note the proper way to quantify the appearance changes is to use chromatic adaptation transforms (CATs), since we are only concerning the color change of the illumination. At the same time, it is found that the chromatic adaptations in the sharper sensor space and in the cone fundamental space provide different results, the ones related to the latter being in better agreement with current blue-to-purple color appearance change. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 322-327, 2013 [ABSTRACT FROM AUTHOR]

It has been reported that for certain colour samples, the chromatic adaptation transform CAT02 imbedded in the CIECAM02 colour appearance model predicts corresponding colours with negative tristimulus values (TSVs), which can cause problems in certain applications. To overcome this problem, a mathematical approach is proposed for modifying CAT02. This approach combines a non-negativity constraint for the TSVs of corresponding colours with the minimization of the colour differences between those values for the corresponding colours obtained by visual observations and the TSVs of the corresponding colours predicted by the model, which is a constrained non-linear optimization problem. By solving the non-linear optimization problem, a new matrix is found. The performance of the CAT02 transform with various matrices including the original CAT02 matrix, and the new matrix are tested using visual datasets and the optimum colours. Test results show that the CAT02 with the new matrix predicted corresponding colours without negative TSVs for all optimum colours and the colour matching functions of the two CIE standard observers under the test illuminants considered. However, the accuracy with the new matrix for predicting the visual data is approximately 1 CIELAB colour difference unit worse compared with the original CAT02. This indicates that accuracy has to be sacrificed to achieve the non-negativity constraint for the TSVs of the corresponding colours. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011 [ABSTRACT FROM AUTHOR]

Data were obtained for the colour appearance of unrelated colours under photopic and mesopic conditions. The effects of changes in luminance level and stimulus size were investigated. The method used was magnitude scaling of brightness, colourfulness, and hue. Two stimulus sizes (10° and 0.5°) and four starting luminance levels (60, 5, 1, and 0.1, cd/m2) were used. The results at 0.1 cd/m2 had large variations, so data were obtained for two additional stimulus sizes (1° and 2°) at this luminance level. Ten observers judged 50 unrelated colours. A total of 17,820 estimations were made. The observations were carried out in a completely darkened room, after 20 min adaptation; each test colour was presented on its own. Brightness and colourfulness were found to decrease with decreases of both luminance level and stimulus size. The CAM97u model predicted brightness more accurately than CIECAM02 but gave worse performance in predicting colorfulness. For hue, CAM97u and CIECAM02 both gave satisfactory predictions. Using the brightness correlate from CAM97u, a new colour-appearance model based on CIECAM02 was developed specifically for unrelated colours under photopic and mesopic conditions, with parameters to allow for the effects of luminance level and stimulus size. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011; [ABSTRACT FROM AUTHOR]

A new colour rendering index, CRI-CAM02UCS, is proposed. It predicts visual results more accurately than the CIE CIR-Ra. It includes two components necessary for predicting colour rendering in one metric: a chromatic adaptation transform and uniform colour space based on the CIE recommended colour appearance model, CIECAM02. The new index gave the same ranks as those of CIE-Ra in the six lamps tested regardless the sample sets used. It was also found that the methods based on the size of colour gamut did not agree with those based on the test-sample method. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2012 [ABSTRACT FROM AUTHOR]

The visual phenomenon known as the colour size effect was investigated and two models were developed to predict the change in colour appearance of samples with six different sizes. The models are capable of transforming the colour appearance of a stimulus having a viewing field of 2° to that associated with a range of viewing fields. They are named the size effect correction and the size effect transform and are based on human perceptual attributes and human cone responses, respectively. The performance of both models was tested using the experimental data, and the results showed that the size effect transform performed better than the size effect correction. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2012 [ABSTRACT FROM AUTHOR]