A structural comparison of the Munsell renotation and the OSA–UCS uniform color systems

A structural comparison has been made of the lightness, chroma, and hue scales of the Munsell system, as expressed in the Munsell Renotations, and of the OSA‐UCS system. While the lightness scales are similar (except for the adjustment for the Helmholtz–Kohlrausch effect and the inclusion of a “crispening” effect in OSA–UCS), there are significant differences in the chroma scales along the major chromatic axes. Unlike in CIELAB, the increments in X and Z along these axes for equal chroma steps in both systems do not fall on a continuous function. In the two systems, as well as in CIELAB lines connecting colors of equal chroma differences at different Y values point to nonreal origins. These differ among the three systems. A major difference between Munsell and OSA–UCS is the size of the first chroma step away from gray. An experiment has been performed with the result that the OSA–UCS system is in much better agreement with the average observer in this respect than the Munsell system. OSA–UCS exhibits considerably more internal uniformity in terms of X and Z increments between steps than the Munsell system. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 186–192, 2000     

Exact location of consensus and consistency colors in the osa‐ucs for the italian language

In a previous work, the authors reported on the results of a color naming experiment performed on native Italian speakers regarding the location of focal colors and centroids in the Uniform Color Scales of the Optical Society of America color system. That work was aiming at comparing such data with those previously obtained by Boynton and Olson (B&O) accounting for the differences in the paradigm and the language. The number of consistency and consensus colors in the different lightness plans was also reported but no information was provided on their placement. Though, such information is very important for any subsequent modeling stage. The objective of this article is to fill such a gap and share such data with the scientific community to provide a reference database for future investigation. Three different datasets were considered: the extended OSA (E‐OSA), the reduced OSA (R‐OSA), and the B&O's (B&O) sets of reference colors. Results show a good overlap among the locations of the consensus colors in the {L, j, g} color model between B&O and the subset of E‐OSA colors overlapping with the B&O 424 colors (R‐OSA), as well as a strong agreement on consistency. Furthermore, a close proximity among the centroids of homologue regions for the majority of the classes was found. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 437–447, 2013     

Comprehensive color solutions: CAM16, CAT16, and CAM16‐UCS

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.     

The CRI‐CAM02UCS colour rendering index

A new colour rendering index, CRI‐CAM02UCS, is proposed. It predicts visual results more accurately than the CIE CIR‐R_a. 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‐R_a 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     

Semantic interpretation of color differences and color‐rendering indices

A series of visual experiments were carried out to rate the similarity of color appearance of two color stimuli on categorical and continuous semantic rating scales. Pairs of color stimuli included two copies of the same colored real or artificial object illuminated by a test light source and a reference light source. A formula was developed to predict a category of color similarity (e.g., “moderate” or “good”) from an instrumentally measured color difference. Given a numeric value of a color difference between the two members of a pair of colors, for example, 2.07, the formula is able to predict a category of color similarity, for example, “good.” Because color‐rendering indices are based on color differences, the formula could be applied to interpret the values of the new color‐rendering index (n‐CRI or CRI2012) in terms of such semantic categories. This semantic interpretation enables nonexpert users of light sources to understand the color‐rendering properties of light sources and the differences on the numeric scale of the color‐rendering index in terms of regular language. For example, a numeric value of 87 can be interpreted as “good.” © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 252–262, 2014; Published online 14 March 2013 in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/col.21798     

Digital image‐color conversion between different illuminants by color‐constancy actuation in a color‐vision model based on the OSA‐UCS system

In digital image capture, the camera signals produced by the D65 illuminant, once translated into tristimulus values of the CIE 1931 standard colorimetric observer (assuming the Maxwell‐Ives‐Luther criterion is satisfied), are considered good to produce accurate color rendering. An image obtained under any illuminant other than D65 does not appear realistic and the tristimulus values of the camera must be transformed into the corresponding ones produced by the D65 illuminant. This transformation must satisfy color constancy. In this work, the transformation is obtained by a color‐vision model based on the Optical Society of America‐Uniform Color Scales system [Color Res Appl 2005; 30: 31–41] and is represented by a matrix dependent on the adaptation illuminant. This matrix is obtained by minimizing the distance between the pairs of the uniform scale chromatic responses related to the tristimulus values of the 99 different color samples of the SG Gretag‐Macbeth ColorChecker measured under a pair of different illuminants, one of which is the D65. Then any picture captured under a given light source can be translated into the picture of the same scene under the D65 illuminant. Metameric reason allows only approximate solutions. The transformations from Daylight and Planckian illuminants to the D65 illuminant have a very regular dependence on the color temperature, that appears to be the typical parameter for the color conversion. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 412–422, 2013     

An inverse to the Optical Society of America‐Uniform Color System

The Optical Society of America Uniform Color Scales (OSA‐UCS) possesses many salutary properties, but it is presently under‐utilized. The authors believe the reason may be that the transformation from CIE notation to the OSA system presently has no known inverse. In an effort to rectify that lack, the OSA system is presented in its forward transformation, and then an inverse transformation using a Newton–Raphson iterative algorithm is described. The authors' experience with parameters associated with implementing the algorithm is related, and a few worked examples are provided to assist potential users in their implementation. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2011     

Principal hue curves and color difference

The size of perceptual difference of colors (j, k) is scaled as d_jk by selecting a pair of Munsell grays in which the lightness difference matches in size with the color difference. Hence, d is given in terms of Munsell V. The degree of principal hue component α in a color j is scaled as ξ_α(j) by making marks on a line segment and the range of ξ_α is from 0 to 10. By plotting ξ_α(H V/C) on Munsell H‐circle, principal hue curves ξ¯_α(H V/C) are defined, where α = R, Y, G, B, V = 4–7, and C = 2–10. In this process, similar plots of NCS codes (cϕ_α) are used as references. The curves ξ¯_α(H V/C) tell us the appearance of Munsell colors (H V/C) and also enable us to predict color differences. The relationship between d_jk and ΔV = |V_j − V_k|, Δξ¯_α = |ξ¯_α(H_j V_j/C_j) − ξ¯_α(H_k V_k/C_k)| is tested in various ways, e.g., logarithmic, power, Minkowski‐type functions. The best predictor d̃ is given by a simple linear form, d̃ = a_VΔV + {d₀ + Σa_αΔξ¯_α}. For 899 pairs (j, k), 706 differing in H, C and 193 differing in H, V, C, a_V = 0.459, d₀ = 0.610, a_R = 0.199, a_Y = 0.031, a_G = 0.098, a_B = 0.136, and the root‐mean‐squares of (d_jk − d̃_jk) is 0.338 in the matched V‐unit. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 266–279, 1999     

Testing CIELAB‐based color‐difference formulas

The CMC, BFD, and CIE94 color‐difference formulas have been compared throughout their weighting functions to the CIELAB components ΔL*, ΔC*, ΔH*, and from their performance with respect to several wide datasets from old and recent literature. Predicting the magnitude of perceived color differences, a statistically significant improvement upon CIELAB should be recognized for these three formulas, in particular for CIE94. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 49–55, 2000     

Mathematical determination of the numerical data corresponding to the color‐matching functions of three real observers using the RGB CIE‐1931 primary system and a new system of unreal primaries X′Y′Z′

In this work, we determine the numerical data of the experimental color‐matching functions (cmf's) of three real observers (JAM, MM, and CF) for two small fields (2°). In previous works, these cmf's have been shown generically and expressed only in a new system of unreal X′Y′Z′ primaries. Here, we show results found with these cmf's for the visible spectrum in intervals of 10 nm, from 400 to 700 nm. The data refer to both the RGB CIE‐1931 system and a new system of unreal primaries X′Y′Z′, established by a procedure similar to that of the XYZ CIE‐1931 system. This transformation was needed, because negative values appeared in various cmf's when they were referred to the XYZ CIE‐1931 system. Recently, we have called this new system G94 (Granada ‘94). Here, we also describe the method and calculation of the matrix that enables this transformation; in testing six real observers with new cmf’s, we found positive results. We have used these new and experimental cmf's in several preceding works, as have other authors as well, to whom J. A. Martínez privately communicated the corresponding numerical data. The use of these cmf's by all the authors has led to noteworthy results. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 89–95, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10127     

Geodesic calculation of color difference formulas and comparison with the munsell color order system

Riemannian metric tensors of color difference formulas are derived from the line elements in a color space. The shortest curve between two points in a color space can be calculated from the metric tensors. This shortest curve is called a geodesic. In this article, the authors present computed geodesic curves and corresponding contours of the CIELAB ( ), the CIELUV ( ), the OSA‐UCS (ΔE_E) and an infinitesimal approximation of the CIEDE2000 (ΔE₀₀) color difference metrics in the CIELAB color space. At a fixed value of lightness L*, geodesic curves originating from the achromatic point and their corresponding contours of the above four formulas in the CIELAB color space can be described as hue geodesics and chroma contours. The Munsell chromas and hue circles at the Munsell values 3, 5, and 7 are compared with computed hue geodesics and chroma contours of these formulas at three different fixed lightness values. It is found that the Munsell chromas and hue circles do not the match the computed hue geodesics and chroma contours of above mentioned formulas at different Munsell values. The results also show that the distribution of color stimuli predicted by the infinitesimal approximation of CIEDE2000 (ΔE₀₀) and the OSA‐UCS (ΔE_E) in the CIELAB color space are in general not better than the conventional CIELAB (ΔE) and CIELUV (ΔE) formulas. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 259–266, 2013     

RIT‐DuPont supra‐threshold color‐tolerance individual color‐difference pair dataset

The RIT‐DuPont dataset has been used extensively for formula development and testing since its inception during the 1980's, for example, in the development of CIE94 and CIEDE2000. The dataset was published as 156 color‐tolerances, T₅₀, along specific vector directions about 19 color centers. Probit analysis was used to transform judgments of 958 color‐difference pairs by 50 observers to these 156 tolerances. For most statistical significance testing, the number of samples determines the confidence limits. Thus, there was an interest in publishing the individual color‐difference pair visual and colorimetric data to improve the precision of significance testing. From these 958 pairs, 828 pairs had determinable visual differences. The others had either excessive visual uncertainty or had unanimous visual judgments such that visual differences were undefined. In addition, a method was devised to assign visual uncertainty to each of these pairs using the principles of maximum likelihood and the T₅₀ values. Comparisons were made between the T₅₀ and individual color‐difference pair data both including and omitting uncertainty weightings. The weighted dataset was found to be equivalent to the T₅₀ tolerances. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2010     

A comparison of five color order systems

Five color order systems (Munsell Renotations, Munsell Re‐renotations, OSA‐UCS, NCS, and Colorcurve) have been compared by optimizing the powers applied to individual opponent‐color functions. The results indicate general similarities in that powers applied to the red and green functions tend to be closer to 1, while those applied to the blue function and the yellow function are generally smaller. Specifically, there are many individual differences that make each system unique. The results inspire confidence in the veracity of the opponent‐color system methodology. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 123–131, 2000     

Are chroma tolerances dependent on hue‐angle?

Relationships between suprathreshold chroma tolerances and CIELAB hue‐angles have been analyzed through the results of a new pair‐comparison experiment and the experimental combined data set employed by CIE TC 1–47 for the development of the latest CIE color‐difference formula, CIEDE2000. Chroma tolerances have been measured by 12 normal observers at 21 CRT‐generated color centers L*₁₀ = 40, C*_ab,10 = 20 and 40, and h_ab,10 at 30° regular steps). The results of this experiment lead to a chroma‐difference weighting function with hue‐angle dependence W_CH, which is in good agreement with the one proposed by the LCD color‐difference formula [Color Res Appl 2001;26:369–375]. This W_CH function is also consistent with the experimental results provided by the combined data set employed by CIE TC 1–47. For the whole CIE TC 1–47 data set, as well as for each one of its four independent subsets, the PF/3 performance factor [Color Res Appl 1999;24:331–343] was improved by adding to CIEDE2000 the W_CH function proposed by LCD, or the one derived by us using the results of our current experiment together with the combined data set employed by CIE TC 1–47. Nevertheless, unfortunately, from the current data, this PF/3 improvement is small (and statistically nonsignificant): 0.3 for the 3657 pairs provided by CIE TC 1–47 combined data set and 1.6 for a subset of 590 chromatic pairs (C*_ab,10>5.0) with color differences lower than 5.0 CIELAB units and due mainly to chroma. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 420–427, 2004; Published online in Wiley Interscience (www.interscience.wiley.com). DOI 10.1002/col.20057     

Media‐dependent color appearance modeling based on artificial neural networks

In this study, we tried to consider various color appearance factors and device characterization together by visual experiment to simplify the across‐media color appearance reproduction. Two media, CRT display (soft‐copy) and NCS color atlas (hard‐copy), were used in our study. A total of 506 sample pairs of RGB and HVC, which are the attributes of NCS color chips, were obtained according to psychophysical experiments by matching soft copy and hard copy by a panel of nine observers. In addition, a set of error back‐propagation neural networks was used to realize experimental data generalization. In order to get a more perfect generalizing effect, the whole samples were divided into four parts according to different hues and the conversion between HVC and R_HVCG_HVCB_HVC color space was implemented. The current results show that the displays on the CRT and the color chips can match well. In this way, a CRT‐dependent reproduction modeling based on neural networks was formed, which has strong practicability and can be applied in many aspects. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 218–228, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20209     

A top down description of S‐CIELAB and CIEDE2000

Recent work in color difference has led to the recommendation of CIEDE2000 for use as an industrial color difference equation. While CIEDE2000 was designed for predicting the visual difference for large isolated patches, it is often desired to determine the perceived difference of color images. The CIE TC8‐02 has been formed to examine these differences. This paper presents an overview of spatial filtering combined with CIEDE2000, to assist TC8‐02 in the evaluation and implementation of an image color difference metric. Based on the S‐CIELAB spatial extension, the objective is to provide a single reference for researchers desiring to utilize this technique. A general overview of how S‐CIELAB functions, as well as a comparison between spatial domain and frequency domain filtering is provided. A reference comparison between three CIE recommended color difference formulae is also provided. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 425–435, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10195     

Performance comparison of JPEG,JPEG 2000, and newly developed CSI‐JPEG by adopting different color models

The major issues of using less storage space and wanting higher transmission rates for information in the form of high quality color images was taken into consideration. Two experiments were conducted in order to investigate and compare performance of compression standard including JPEG 1992 and JPEG 2000, and a newly developed CSI‐JPEG. The CSI‐JPEG is an amalgamation of Cubic Spline Interpolation (CSI) with baseline JPEG 1992 algorithm. The performance of different image compression algorithms was evaluated using different color models/spaces in terms of compression rate, color accuracy, and visual quality. The results from three assessment methods consistently showed that JPEG 2000 and CSI‐JPEG performed significantly better compared with JPEG 1992 for small color differences (in the range of acceptability). Moreover, the CAM02‐UCS performed best among other selected models in terms of compression rate and image performance for all three image compression algorithms. The results from the visual assessment also confirmed this. It was also found that CIEDE2000 can be reliably used for assessing quality of compressed images with low levels of distortion. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 460–473, 2017