Search

Starting from the colour-matching functions measured by Stiles - Burch for a 2? visual field and ten individual observers, published by Trezona we have discussed certain differences between this dataset and the CIE 1931 Standard Observer. Moreover, a Standard Deviate Observer (SDO) has been developed from this dataset, loosely following the method proposed by Nayatani et al. It should be borne in mind that all the SDO previously proposed were derived from colour-matching functions measured with greater sizes of the visual field. D'apr?s les mesures des fonctions colorim?triques de dix observateurs individuels faites par Stiles et Burch pour le champ visuel de deux degr?es, publi?es par Trezona, on a ?tudi? les differences entre l'observateur qui en est consequence et celui qui a ?t? propos? par la CIE (observateur CIE-1931). En plus, on a obtenu l'Observateur D?vi? Standard (SDO), en employant une m?thode pareille de celle qui a ?t? propos?e par Nayatani et al. On ne doit pas oublier que, jusqu'? maintenant, tous les SDO propos?s avaient ?t? obtenus d'apr?s les fonctions colorim?triques pour des champs visuels plus larges.

In the present work, we study the influence of colour-matching functions on the determination of colour-threshold differences (part I) and of greater size (part II). For this, we have used the colour-matching functions of 16 different observers, the five CIE centres and 200 Munsell samples. A previously operated and fully automated Wright colorimeter was used for the determination of colour-matching functions and colour-discrimination thresholds. The results for part I indicate that the discrimination ellipses obtained with different colour-matching functions (with some exceptions) had highly similar orientations, semiaxis relationships and areas, whereas the centres appeared to shift with respect to each other, primarily in the blue area. The results for part II reveal important discrepancies between the different observers in evaluating any colour, and close agreement in evaluating colour differences. On the basis of the overall study, we deduce that the colour-matching functions of the observers have significant influence on the specification of individual colours, but only slight effects on the determination of small or large colour differences. Nous ?tudions dans le pr?sent travail l'influence des fonctions colorim?triques sur la d?termination du seuil de diff?rence de couleur (partie I) et des grandes diff?rences (partie II). Pour ce but, nous avons utilis? les fonctions de 16 observateurs diff?rents cinq centres CIE et 200 ?chantillons de Munsell. Un colorim?tre automatique de Wright a ?t? utilis? pour notres mesures. Les r?sultats de la premi?re partie font appara?tre que les ellipses de discrimination obtenues avec diff?rentes fonctions colorim?triques ont (? quelques exceptions pr?s) des orientations, ellipticit?s et aires similaires, mais sont excentr?es les unes par rapport aux autres, principalement dans le bleu. Les r?sultats de la seconde partie r?v?lent d'importantes diff?rences entre les observateurs pour l'?valuation de chaque couleur, mais non pour les diff?rences de couleur. Les r?sultats de l'ensemble de cette ?tude font appara?tre l'influence importante des fonctions colorim?triques sur la sp?cification des couleurs et faible sur la d?termination des diff?rences petites ou grandes.

Colour-difference formulas are tools employed in colour industries for objective pass/fail decisions of manufactured products. These objective decisions are based on instrumental colour measurements which must reliably predict the subjective colour-difference evaluations performed by observers’ panels. In a previous paper we have tested the performance of different colour-difference formulas using the datasets employed at the development of the last CIErecommended colour-difference formula CIEDE2000, and we found that the AUDI2000 colour-difference formula for solid (homogeneous) colours performed reasonably well, despite the colour pairs in these datasets were not similar to those typically employed in the automotive industry (CIE Publication x038:2013, 465-469). Here we have tested again AUDI2000 together with 11 advanced colour-difference formulas (CIELUV, CIELAB, CMC, BFD, CIE94, CIEDE2000, CAM02-UCS, CAM02-SCD, DIN99d, DIN99b, OSA-GP-Euclidean) for three visual datasets we may consider particularly useful to the automotive industry because of different reasons: 1) 828 metallic colour pairs used to develop the highly reliable RIT-DuPont dataset (Color Res. Appl. 35, 274-283, 2010); 2) printed samples conforming 893 colour pairs with threshold colour differences (J. Opt. Soc. Am. A 29, 883-891, 2012); 3) 150 colour pairs in a tolerance dataset proposed by AUDI. To measure the relative merits of the different tested colour-difference formulas, we employed the STRESS index (J. Opt. Soc. Am. A 24, 1823-1829, 2007), assuming a 95% confidence level. For datasets 1) and 2), AUDI2000 was in the group of the best colour-difference formulas with no significant differences with respect to CIE94, CIEDE2000, CAM02-UCS, DIN99b and DIN99d formulas. For dataset 3) AUDI2000 provided the best results, being statistically significantly better than all other tested colour-difference formulas.

The CIELAB lightness and chroma values of pixels in five of the eight ISO SCID natural images were modified to produce sample images. Pairs of images were displayed on a calibrated monitor and assessed by a panel of 12 observers with normal color vision using a categorical judgment method. The experimental results showed that assuming the lightness parametric factor k(L)=1 to predict color differences in images, CIELAB performed better than CIEDE2000, CIE94, or CMC, which is a different result to the one found in color-difference literature for homogeneous color pairs. However, observers perceived CIELAB lightness and chroma differences in images in different ways. To fit current experimental data, a specific methodology is proposed to optimize k(L) in the color-difference formulas CIELAB, CIEDE2000, CIE94, and CMC. From the standardized residual sum of squares (STRESS) index, it was found that the optimized formulas, CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1), performed significantly better than their corresponding original forms with lightness parametric factor k(L)=1. Specifically, CIEDE2000(2.3:1) performed the best, with a satisfactory average STRESS value of 25.8, which is very similar to the 27.5 value that was found from the CIEDE2000(1:1) formula for the combined weighted dataset of homogeneous color samples employed at the development of this formula [J. Opt. Soc. Am. A25, 1828 (2008), Table 2]. However, fitting our experimental data, none of the four optimized formulas CIELAB(1.5:1), CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1) is significantly better than the others. Current results roughly agree with the recent CIE recommendation that color difference in images can be predicted by simply adopting a lightness parametric factor k(L)=2 in CIELAB or CIEDE2000 [CIE Publication 199:2011]. It was also found that the different contents of the five images have considerable influence on the performance of the tested color-difference formulas.

International audience; 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.

A check of the weighting functions proposed by the CIE Technical Committee 1-29 (TC1-29) [Color Res. Appl. 18, 137–139 (1993)] has been carried out using pairs of samples with only a threshold difference in chroma, hue, or lightness. Experimental results with object and aperture colors were used and some parametric factors can be appointed: the dependence of ΔC* on C* appears to be about two times stronger for related color versus the aperture color mode; and the ΔH* dependence on C*, weak but significant for related colors, becomes insignificant for aperture colors. the hue-angle dependences of ΔH* after correction for C* and the dependences of ΔL* on L* or C* are found not significant for aperture colors. After normalization of the five different datasets employed, the results achieved are in overall good agreement and support the proposal of the CIE TC1-29.

The performances of uniform color spaces and color-difference formulae for predicting threshold color differences were investigated based on visual assessments of 893 pairs of printed color patches under a D65 source. The average ΔE(ab,10)* of the pairs was 1.1 units. A threshold psychophysical experiment was repeated three times by a panel of 16 observers with normal color vision. The experimental data were used to evaluate nine color-difference formulae and uniform color spaces using the standardized residual sum of squares (STRESS) measure. The results indicated that all formulae and spaces performed very similarly to each other, and outperformed CIELAB for threshold color differences. The chromaticity-discrimination ellipses were used to compare with previous results from small color differences [Color Res. Appl. (2011), doi:10.1002/col.20689], and they agreed with each other, except for the purple color center.

For a pair of homogeneous colour samples or two complex images viewed under specific conditions, colour-difference formulas try to predict the visually perceived (subjective) colour difference starting from instrumental (objective) colour measurements. The history related to the five up-to-date CIE-recommended colour-difference formulas is reviewed, with special emphasis on the structure and performance of the last one, CIEDE2000. Advanced colour-difference formulas with an associated colour space (e.g., DIN99d, CAM02, Euclidean OSA-UCS, etc.) are also discussed. Different indices proposed to measure the performance of a given colour-difference formula (e.g., PF/3, STRESS, etc.) are reviewed. Among current trends on colour-difference evaluation, it can be mentioned the research activities carried out by different CIE Technical Committees (e.g., CIE TC’s 1-55, 1-57, 1-63, 1-81 and 8-02), the need of new reliable experimental datasets, the development of colour-difference formulas based on IPT and colour-appearance models, and the concept of “total differences,” which considers the interactions between colour properties and other object attributes like texture, translucency, and gloss.

Threshold-discrimination ellipses were obtained by three normal observers, at five color centers, by the use of two experimental devices, which provided light-surface colors, and the same method and experimental conditions. The ellipses obtained for each center in both devices were quite similar when the sample distributions were appropriate and slightly smaller for object color than for aperture color. The ellipses predicted by three recent color-difference formulas based on the CIE uniform color space (CIELAB) were compared with those experimentally obtained by us as well as by other researchers who analyzed a greater number of color centers. The color-difference formula proposed by the CIE Technical Committee 1-29 [Color Res. Appl. 18, 137 (1993)] provides the best prediction of the semiaxis relationship for all the experimental datasets used, confirming their good performance in previous works [Appl. Opt. 33, 8069 (1993)], although the differences with respect to the experimental results are higher than those attributable to the interobserver variability.

We present a multispectral system developed and optimized for measurement of the spectral reflectance and the color of the human iris. We tested several sets of filters as acquisition channels, analyzed different reconstruction algorithms, and used different samples as training sets. The results obtained show that a conventional three-channel color camera (RGB) was enough to reconstruct the analyzed reflectances with high accuracy, obtaining averaged color differences of around 2-3 CIEDE2000 units and root mean square errors of around 0.01. The device developed was used to characterize 100 real irises corresponding to 50 subjects, 68 prostheses used in clinical practice, and 17 cosmetic colored contact lenses.

New results of chromatic discrimination have been obtained for light transmitted by coloured filters and with a method which guarantees their reproducibility. These results are compared with others obtained for surface-colour presentation through the predictions of several colour-difference formulae: those recommended by CIE (CIELUV and CIELAB), the LABNHU formula, and the cdf-G, cdf-G* formulae, recently proposed by the authors' laboratory.

The values of the coefficients of the metric tensor (g11, g22, and 2g12) have been determined graphically at any point on the CIE-1964 chromatic diagram, by using Luo and Rigg's chromatic discrimination data. Chromatic-discrimination thresholds obtained by this method and those predicted by the CIELUV, CIELAB; and LABNHU color-difference formulas are compared with different sets of experimental results. The predictions made by the formulas do not fit the experimental results as well as the graphical predictions.

This paper illustrates how to design a visual experiment to measure color differences in gonioapparent materials and how to assess the merits of different advanced color-difference formulas trying to predict the results of such experiment. Successful color-difference formulas are necessary for industrial quality control and artificial color-vision applications. A color- difference formula must be accurate under a wide variety of experimental conditions including the use of challenging materials like, for example, gonioapparent samples. Improving the experimental design in a previous paper [Melgosaet al., Optics Express 22, 3458-3467 (2014)], we have tested 11 advanced color-difference formulas from visual assessments performed by a panel of 11 observers with normal colorvision using a set of 56 nearly achromatic colorpairs of automotive gonioapparent samples. Best predictions of our experimental results were found for the AUDI2000 color-difference formula, followed by color-difference formulas based on the color appearance model CIECAM02. Parameters in the original weighting function for lightness in the AUDI2000 formula were optimized obtaining small improvements. However, a power function from results provided by the AUDI2000 formula considerably improved results, producing values close to the inter-observer variability in our visual experiment. Additional research is required to obtain a modified AUDI2000 color-difference formula significantly better than the current one., This research was supported by the Ministry of Economy and Competitiveness of Spain, research projects FIS2013-40661-P and DPI2011-30090-C02, with European Research Development Fund (ERDF), as well as by the National Science Foundation of China (grant number 61178053).

An investigation of the color metrics and the complexity of the CIEDE2000 formula shows that CIELAB space is inadequate to represent small-medium color differences. The OSA-UCS (Uniform Color Space) Committee has shown that no space with uniform scale for large color differences exists. Therefore the practical way for color-difference specification is a color-difference formula in a nonuniform space. First, the BFD (Bradford University) ellipses are considered in the OSA-UCS space, and their very high regularity suggests a new and very simple color-difference formula at constant luminance. Then the COM (combined) data set used for the development of the CIEDE2000 formula is considered in the OSA-UCS space, and the color-difference formula is extended to sample pairs with a different luminance factor. The value of the performance factor PF/3 for the proposed OSA-UCS-based formula shows that the formula performs like the more complex CIEDE2000 formula for small-medium color differences.

Scattered colorimetry, i.e., multi-angle and multi-wavelength absorption spectroscopy performed in the visible spectral range, was used to map three kinds of liquids: extra virgin olive oils, frying oils, and detergents in water. By multivariate processing of the spectral data, the liquids could be classified according to their intrinisic characteristics: geographic area of extra virgin olive oils, degradation of frying oils, and surfactant types and mixtures in water.

CIELAB-based color-difference formulas are used to improve the prediction of visually perceived color differences through the introduction of various corrections to CIELAB. In our study we analyze the relative importance of these corrections. From the combined dataset employed for the development of CIEDE2000, we found that the improvement of CIE94 over CIELAB was considerably greater than that of CIEDE2000 over CIE94. Chroma-difference correction was the most important correction in both CIE94 and CIEDE2000. With an arbitrary value of 100 assigned to this correction, the score of the hue-difference correction in CIE94 was 21, and the scores of the four remaining corrections in CIEDE2000 were as follows: hue difference, 29; rotation term, 8; lightness difference, 8; and gray correction, 6. At 95% confidence level each of the corrections introduced in CIEDE2000 or CIE94 was statistically significant for the whole combined dataset, in agreement with the results reported by CIE TC 1-47 and 1-29. For the combined dataset, the differences between CMC and CIEDE2000 were found to be statistically significant at 95% confidence level, but the differences between CMC and CIE94 were not. From subsets of the combined dataset it was concluded that further analyses of the lightness-difference and gray corrections proposed by CIEDE2000 would be desirable, using new experimental data.

Spectral transmittances of three new photochromatic lenses have been measured at different activation states produced by the four luminous sources of a VeriVide CAC 120 cabinet. The greatest transmittance changes were found for the UV and D65 sources. These transmittances changes lead to average CIELAB color differences lower than 6 CIELAB units for the 24 chips of a GretagMacbeth color chart.

We have performed spectroradiometric color measurements at different positions on the floor of two lights booths under two light sources. Uniformity provided by these light booths is enough acceptable for most practical color applications involving relative measurements (e.g. CIELAB coordinates), but not for absolute measurements (e.g. tristimulus values or illuminance). Averge color inconstancy indices are lower than 0.6 CIE94 (1.0 CIELAB) color-difference units.

After each laboratory session, students must answer individually three random multiple choice questions. The corresponding software has been developed by us. This self-evaluation test motivates students before and during sessions performance, and provides objective information to the teacher. The estimated students’ mean satisfaction with this system is 8.1/10.

Se han realizado medidas por reflexión del color de 200 muestras de aceite de oliva virgen de las zonas de producción sur de España y zona norte de Marruecos. También se han realizado medidas del grado de acidez de los mismos y se emplea el análisis cluster para el estudio de su clasificación. Los resultados obtenidos muestran que, en general, los aceites marroquíes presentan con respecto a los españoles, tonalidades más verdosas (hº medio de 87,3º frente a 85,1º) y son menos saturados (C* medio de 91,7 frente a 105,3). Por otra parte, el grado de acidez difiere notablemente para ambas zonas geográficas, con valores medios de 0,31 para los aceites españoles y 2,87 para los marroquíes. A partir de estas medidas experimentales el análisis cluster muestra que, es posible clasificar los aceites de oliva según su zona geográfica de producción (hispano-marroquí), en función del grado de acidez ó del ángulo de tono., Colour measurements by reflection of 200 virgin olive oil samples from the southern zone of Spain and the northern one of Morocco were made. Acidity measurements of these oils were made as well, and a cluster analysis was employed for their classification. Results show that, generally, Moroccan oils present a hue more greenish (hº average of 87.3º) than the Spanish ones (85.1º), and those are less saturated (C* average of 91.7 and 105.3, respectively). On the other hand, acidity is remarkably different for these two geographic zones, having average values of 0.31 for Spanish oils and 2.87 for Moroccan ones. From these experimental measurements, the cluster analysis shows that it is possible to classify these oils according to their geographic production zones (Spanish-Moroccan area) depending on the acidity or the hue angle., Esta trabajo ha sido realizado gracias al Proyecto 17PRO/00 financiado por la Agencia Española de Cooperación Internacional (AECI).

Random errors were propagated form tristimulus values to CIELAB and CIELUV color coordinates, using the instrumental color measurements of 28 Munsell samples illuminated by 3 light sources and performed by 3 different procedures. The errors found for the CIELAB and CIELUV color coordinates are considerably different for the 3 procedures. These errors are highest for the F source, and similar for TL84 and D65. Under our experimental conditions, we obtain that the instrumental errors for the a and b coordinates are lower than the visual subthresholds predicted by the CIE94 model.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An experimental study of Allergan's Oxysept Comfort system was performed by measuring the slight reddish hue that appears in the disinfecting solution, indicating to the users that their lenses are again ready to be worn. The temporal evolution of the color of the solution has been measured under standardized conditions and analyzed in the CIELAB system, from the perspective of the typical threshold discrimination of the human eye. Color differences between neutralized and non-neutralized solutions occurred in an appropriate direction of the color space to enhance discrimination and were clearly perceptible by normal observers (greater than 9.7 ± 1.2 CIELAB units). Colorimetric analyses have been used to draw conclusions regarding observers with defective color vision. The color of the solution changes abruptly, approximately 25 min after the neutralization process begins, and remains nearly constant after about 60 min, this agreeing well with the temporal evolution of the hydrogen peroxide concentration.

In this paper, two types of weighting tables are derived by applying the local power expansion method proposed by Oleari [Color Res. Appl.25, 176 (2000)CREADU0361-231710.1002/(SICI)1520-6378(200006)25:3 3.0.CO;2-Z]. Both tables at two different levels consider the deconvolution of the spectrophotometric data for monochromator triangular transmittance. The first one, named zero-order weighting table, is similar to weighting table 5 of American Society for Testing and Materials (ASTM) used with the measured spectral reflectance factors (SRFs) corrected by the Stearns and Stearns formula. The second one, named second-order weighting table, is similar to weighting table 6 of ASTM and must be used with the undeconvoluted SRFs. It is hoped that the results of this paper will aid the International Commission on Illumination TC 1-71 on tristimulus integration in focusing on ongoing methods, testing, and recommendations.

The most widely used color-difference formulas are based on color-difference data obtained under D65 illumination or similar and for a 10° visual field; i.e., these formulas hold true for the CIE 1964 observer adapted to D65 illuminant. This work considers the psychometric color-vision model based on the Optical Society of America–Uniform Color Scales (OSA-UCS) system previously published by the first author [J. Opt. Soc. Am. A21, 677 (2004)JOAOD60740-323210.1364/JOSAA.21.000677; Color Res. Appl.30, 31 (2005)CREADU0361-231710.1002/col.20072] with the additional hypothesis that complete illuminant adaptation with perfect color constancy exists in the visual evaluation of color differences. In this way a computational procedure is defined for color conversion between different illuminant adaptations, which is an alternative to the current chromatic adaptation transforms. This color conversion allows the passage between different observers, e.g., CIE 1964 and CIE 1931. An application of this color conversion is here made in the color-difference evaluation for any observer and in any illuminant adaptation: these transformations convert tristimulus values related to any observer and illuminant adaptation to those related to the observer and illuminant adaptation of the definition of the color-difference formulas, i.e., to the CIE 1964 observer adapted to the D65 illuminant, and then the known color-difference formulas can be applied. The adaptations to the illuminants A, C, F11, D50, Planckian and daylight at any color temperature and for CIE 1931 and CIE 1964 observers are considered as examples, and all the corresponding transformations are given for practical use.

The standardized residual sum of squares index was proposed to examine the significant merit of a given color-difference formula over another with respect to a given set of visual color-difference data [J. Opt. Soc. Am. A 24, 1823-1829, 2007]. This index can also be employed to determine intra- and inter-observer variability, although the full complexity of this variability cannot be described by just one number. Appropriate utilization of the standardized residual sum of squares index for the assessment of observer variability is described with a view to encourage its use in future color-difference research. The main goal of this paper is to demonstrate that setting the F parameters of the standardized residual sum of squares index to 1 results in a loss of essential properties of the index (for example, symmetry), and is therefore strongly discouraged.

The standardized residual sum of squares (STRESS) index was used to reevaluate four experimental datasets employed during the development of CIEDE2000, the current CIE recommended color-difference formula. This index enables statistical inferences not achievable by other metrics used commonly for performance evaluation. It was found that CIEDE2000 was statistically superior at a 95% confidence level to either CIE94, the previous recommended equation by the CIE, or the simple Euclidean distance in CIELAB, DeltaE*ab. Recent formulas based on the CIECAM02 color-appearance space and chroma-compressed variants of CIELAB were also evaluated and found to have only slightly reduced performance compared with CIEDE2000. These formulas have the advantage of simplicity and easier interpretation when used for quantifying color accuracy. Finally, each experimental dataset was evaluated separately rather than weight averaged as used during the development of CIEDE2000. Significant differences were found between datasets, suggesting that combining datasets may obscure important differences and that the practice of parameter optimization during formula development using combined data is likely suboptimal.

The Technical Committee 1-55 of the International Commission on Illumination on “Uniform color space for industrial color difference evaluation” is requesting the submission of datasets for use in developing a new approximately uniform color space for industrial use. The data should be submitted to the TC Chair, Dr. Manuel Melgosa at the University of Granada. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 159, 2007

We have analyzed the way in which simulated textures made of random dots influence visual suprathreshold color tolerances. We considered 32 textures created by a systematic variation of the following variables: size, number, and color of the dots. Each texture was mapped on the five centers recommended by the International Commission on Illumination (CIE) in 1978 [Color Res. Appl.3, 149 (1978)]. A panel of five experienced observers determined the experimental tolerances using a CRT color monitor by the method of adjustment. At our observation distance, neither small differences in dot size (1 or 4 pixels) nor sparse number of dots (less than 20% of the surface of the sample) changed the tolerances found for homogeneous samples. For the textures that led to statistically significant differences with respect to homogeneous samples, the parametric factors of CIEDE2000 and CIE94 color-difference formulas were fitted. These simulated textures consistently reduced the color difference perceived in a pair or, equivalently, increased the tolerances, mainly (but not only) lightness tolerance. The results demonstrate that it is not simple to provide a unique set of parametric factors for all the potential textures.

This paper presents colourimetric analyses of 6 standard soil-colour charts (1372 chips) from different manufacturers, editions, and degrees of use. The CIELAB hab, L*, and C*ab were found to have significant (analysis of variance, P < 0.05) variations among tested charts, and the Munsell hue, value, and chroma measured in most chips varied from their notation by as much as 1 unit. This discrepancy can be attributed to printing differences and/or colour fading. The Munsell loci of constant hue and chroma plotted in CIELAB colour space showed that colour fading is not uniform, so that visual steps between neighbouring chips change, and constant hue and chroma lines become deformed. The colour difference between chips identically designated in two charts ranged from 0.94 CIEDE2000 units (above perception threshold) for charts from the same manufacturer and degree of use, to 3.72 CIEDE2000 units for old charts from 2 different manufacturers. Chips from old charts became yellowish, darker, and less saturated. These colour changes are consistent with the responses of 10 observers who, on assessing 10 soil-colour samples, reported Munsell notations to have redder hue, lighter value and greater chroma. Periodic colourimetric checking of soil-colour charts would be advisable in order to avoid mistakes in soil-colour description.

The Rochester Institute of Technology-Dupont dataset [Color Res. Appl. 16, 297-316 (1991)] has been used to analyze the uniformity of seven color metrics, developed after CIELUV and CIELAB, with methods similar to those previously applied to several other classical datasets [J. Opt. Soc. Am. A 9, 1247-1253 (1992)]. Significant performance improvements over CIELAB were found with several CIELAB-based metrics, mainly with the model recently proposed by Commission Internationale de L'Eclairage Technical Committee 1-29 [Color Res. Appl. 18, 137-139 (1993)]. Several significant differences found between some pairs of metrics became insignificant when we selected from the Rochester Institute of Technology-Dupont dataset pairs of samples with only chromaticity differences.

An evaluation of chromatic and color-threshold differences was carried out by using the CMC(l:c) and the BFD(l:c) color-difference formulas [ J. Soc. Dyers Colour.100, 128 ( 1984); J. Soc. Dyers Colour.103, 86 ( 1987)] from experimental threshold results obtained with visual colorimeters [ J. Opt. Soc. Am.32, 247 ( 1942); J. Opt. Soc. Am.39, 808 ( 1949); J. Opt. Soc. Am.47, 137 ( 1957); J. Opt. Soc. Am.61, 1135 ( 1971)]. The good results previously reported on the use of these two new formulas with surface colors are also found for the chromatic differences calculated, especially with the BFD(1:1) formula. In the case of color differences BFD(0.6:1) improves the results achieved with BFD(1:1) and gives results similar to those of CMC(1:1). The claim of CMC(l:c) and BFD(l:c) as sometimes superior alternatives to the currently recommended CIE formulas CIELUV and CIELAB can be supported by the present results.

Chromatic-discrimination thresholds have been determined by use of a CRT color monitor that simulates aperture and object observation modes. The results were compared with earlier ones [Appl. Opt. 35, 176 (1996)] that were obtained under the same experimental observation conditions with different devices. The differences found between the results obtained with the CRT monitor and with visual colorimeters are of the order of magnitude of interobserver variability (9%). However, greater differences were found between results for CRT monitors and for illuminated samples, which could be attributed to suboptimal distribution of the samples in color space. The current results support the usefulness of CRT color monitors in new chromatic-discrimination experiments.

The perception and understanding of the three color attributes have been analyzed from two experiments using pairs of Munsell samples, where only one of the three color attributes were changed/unchanged (Experiment I/II) at a time. In each experiment, 36 pairs with color differences of 3 different sizes (average values of 15.8 and 21.7 CIELAB units for Experiments I and II, respectively) were assessed under standardized conditions by 40 normal observers, 20 of them with previous knowledge and experience in colorimetry. At a 95% confidence level, the results from the two experiments were not significantly different, indicating that color attributes were not easily distinguished: for example, for experienced observers, the percentage of correct answers for identifying the color attribute responsible for a color difference was only 72.4%, the random probability being 33.3%. There were no significant differences between the results found by men and women. The worst distinguished attribute was Chroma, that is, the least frequent confusion was between Hue and Value or vice versa. Value differences were more easily detected for achromatic than for chromatic pairs, both for experienced and inexperienced observers. With respect to the size of the color differences, we observed that large hue differences were more easily identifiable than smaller ones, and a constant Hue was more identifiable when the entire color difference was small. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 356–367, 2000

We tested a new spectroradiometric method of color measurement in soil samples and found that the main advantage of this method is that it determines reliably the color of undisturbed soil fragments. To standardize the process, and to compare the accuracy of the spectroradiometric measurements with the results of visual determinations, we measured color in natural soil aggregates and in ground-homogenized samples and represented the results in the CIE 1976 (L * a * b * ) color space. Variability in colors in a given aggregate was evaluated with the parameter and was, on average, 10-fold greater than for ground samples. Grinding and homogenization of soil materials resulted in significant changes in color (18.3 CIELAB units in average), 89% of this color difference being attributable to an increase in lightness. Visual measurements of color with standard soil color charts showed an interobserver variability very similar for disturbed and undisturbed samples. However, color differences between the instrumental measurements of soil samples and the average visual judgments were 4.41 and 10.19 CIELAB units for disturbed and undisturbed samples, respectively, with the greatest errors in the estimation of the L * coordinates in both cases.

We have tested the potential improvement achieved by the CIE94 color-difference model [CIE Publication 116–1995] when a linear function of lightness (SL = K1 + K2 L*) is introduced as a weighting function for the lightness difference ΔL*. Our analyses are based on experimental results previously obtained using object colors and visual colorimeters; new experimental results are not reported here. For the RIT-Dupont dataset [Color Res. Appl. 16, 297–316, 1991], the optimal values of the linear function tested are K1 = 1.02; K2 = 0.001, in excellent agreement with the proposal of the CIE94 model: K1 = 1.0; K2 = 0. However, for the dataset of Coates et al. [J. Soc. Dyers Colour. 97, 179–183, 1981], the best predictions were obtained using K1 = 0.5; K2 = 0.008, improving the results obtained by CMC(l:c) or BFD(l:c). These last values differ by a factor two from the proposal K1 = 1.0; K2 = 0.017 made in another recent study [Color Res. Appl. 19, 273–276, 1994]. Parametric factors, such as the texture of the samples and the lightness of the background, appear to be the main causes of the discrepancies found between the best weighting function for lightness, using different experimental datasets. While new experimental results are reported to clarify this problem, the present results support the proposal made by the CIE94 model, suggesting that the lightness-dependence correction can be considered as nonrobust and related to local parametric effects. © 1996 John Wiley & Sons, Inc.

In this article, we report new color discrimination ellipsoids calculated from two normal observers, using a CRT device and five values of luminance at each of the five centers recommended by the CIE in 1978 (Col Res Appl 1978;3:149–151). Our main goal was to test the weighting function for lightness adopted by the CIE94 color-difference model (CIE Publication 116, 1995). Although some of the experimental conditions employed here (CRT monitor, small size of the visual field, and controlled exposure time) did not fit those recommended by this model, our results support the weighting function for lightness proposed by CIE94. The only robust trends observed in the ellipsoids obtained were a confirmation of Weber’s law and a decrease in the area of the x, y chromaticity ellipses, when the luminance of each reference stimulus increased towards the one of the surround. © 1999 John Wiley & Sons, Inc. Col Res Appl

Three replicates were prepared for each of 60 BTB (bromthymol blue) standards, which are usually employed to determine the color of virgin olive and seed oils, and their colors were measured by spectrophotometric and spectroradiometric techniques on a monthly basis over a year. Although in principle both techniques are valid, their results are weakly correlated. The major color change of the BTB standards occurred soon after sample preparation; after 5 mon, the color stabilized at approximately 3.0 Commission Internationale de l’Eclairage 1976-(L*a*b*) (CIELAB) units, with respect to the initial values. Therefore, after preparation, a certain waiting period would be advisable before using the BTB standards. The color of the BTB standards changes over time in the sense of becoming lighter, more saturated, and less greenish. In the monthly periods after the fifth month, the average color change of the BTB standards was negligible, being slightly lower than the average variability of the three replicates (which is around 1.5 CIELAB units).

The current color-difference formulas were developed based on 2D samples and there is no standard guidance for the color-difference evaluation of 3D objects. The aim of this study was to test and optimize the CIELAB and CIEDE2000 color-difference formulas by using 42 pairs of 3D-printed spherical samples in Experiment I and 40 sample pairs in Experiment II. Fifteen human observers with normal color vision were invited to attend the visual experiments under simulated D65 illumination and assess the color differences of the 82 pairs of 3D spherical samples using the gray-scale method. The performances of the CIELAB and CIEDE2000 formulas were quantified by the STRESS index and F-test with respect to the collected visual results and three different optimization methods were performed on the original color-difference formulas by using the data from the 42 sample pairs in Experiment I. It was found that the optimum parametric factors for CIELAB were kL = 1.4 and kC = 1.9, whereas for CIEDE2000, kL = 1.5. The visual data of the 40 sample pairs in Experiment II were used to test the performance of the optimized formulas and the STRESS values obtained for CIELAB/CIEDE2000 were 32.8/32.9 for the original formulas and 25.3/25.4 for the optimized formulas. The F-test results indicated that a significant improvement was achieved using the proposed optimization of the parametric factors applied to both color-difference formulas for 3D-printed spherical samples., ApPEARS (Appearance Printing European Advanced Research School) European Commission 814158, Spanish Government, European Commission PID2019-107816GB-I00/SRA/10.13039/501100011033