Comparison of the metrics between the CIELAB and the DIN99 uniform color spaces using dental resin composite material values

The sizes for the perceptible or acceptable color difference measured with instruments vary by factors such as instrument, material, and color‐difference formula. To compensate for disagreement of the CIELAB color difference (ΔE^*_ab) with the human observer, the CIEDE2000 formula was developed. However, since this formula has no uniform color space (UCS), DIN99 UCS may be an alternative UCS at present. The purpose of this study was to determine the correlation between the CIELAB UCS and DIN99 UCS using dental resin composites. Changes and correlations in color coordinates (CIE L*,a*, and b* versus L₉₉, a₉₉, and b₉₉ from DIN99) and color differences (ΔE^*_ab and ΔE₉₉) of dental resin composites after polymerization and thermocycling were determined. After transformation into DIN99 formula, the a value (red–green parameter) shifted to higher values, and the span of distribution was maintained after transformation. However, the span of distribution of b values (yellow–blue parameter) was reduced. Although color differences with the two formulas were correlated after polymerization and thermocycling (r = 0.77 and 0.68, respectively), the color coordinates and color differences with DIN99 were significantly different from those with CIELAB. New UCS (DIN99) was different from the present CIELAB UCS with respect to color coordinates (a and b) and color difference. Adaptation of a more observer‐response relevant uniform color space should be considered after visual confirmation with dental esthetic materials. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 168–173, 2006     

Unique hue data for colour appearance models. Part III: Comparison with NCS unique hues

In this study, Swedish Natural Color System (NCS) unique hue data were used to evaluate the performance of unique hue predictions by the CIECAM02 colour appearance model. The colour appearance of 108 NCS unique hue stimuli was predicted using CIECAM02, and their distributions were represented in a CIECAM02 a_c–b_c chromatic diagram. The best‐fitting line for each of the four unique hues was found using orthogonal distance regression in the a_c–b_c chromatic diagram. Comparison of these predicted unique hue lines (based on the NCS data) with the default unique hue loci in CIECAM02 showed that there were significant differences in both unique yellow (UY) and unique blue (UB). The same tendency was found for hue uniformity: hue uniformity is worse for UY and UB stimuli in comparison with unique red (UR) and unique green (UG). A comparison between NCS unique hue stimuli and another set of unique hue stimuli (obtained on a calibrated cathode ray tube) was conducted in CIECAM02 to investigate possible media differences that might affect unique hue predictions. Data for UY and UB are in very good agreement; largest deviations were found for UR. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 256–263, 2015     

Evaluation of performance of various color‐difference formulae using an experimental black dataset

The objective of this study was to develop a specific visual dataset comprising black‐appearing samples with low lightness (L* ranging from approximately 10.4 to 19.5), varying in hue and chroma, evaluating their visual differences against a reference sample, and testing the performance of major color difference formulas currently in use as well as OSA‐UCS‐based models and more recent CAM02 color difference formulas including CAM02‐SCD and CAM02‐UCS models. The dataset comprised 50 dyed black fabric samples of similar structure, and a standard (L*= 15.33, a* = 0.14, b* = ?0.82), with a distribution of small color differences, in ΔE^*_ab, from 0 to approximately 5. The visual color difference between each sample and the standard was assessed by 19 observers in three separate sittings with an interval of at least 24 hours between trials using an AATCC standard gray scale for color change, and a total of 2850 assessments were obtained. A third‐degree polynomial equation was used to convert gray scale ratings to visual differences. The Standard Residual Sum of Squares index (STRESS) and Pearson's correlation coefficient (r), were used to evaluate the performance of various color difference formulae based on visual results. According to the analysis of STRESS index and correlation coefficient results CAM02 color difference equations exhibited the best agreement against visual data with statistically significant improvement over other models tested. The CIEDE2000 (1:1:1) equation also showed good performance in this region of the color space. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 589–598, 2014     

CIELAB color space boundaries under theoretical spectra and 99 test color samples

A color space is a three-dimensional representation of all the possible color percepts. The CIE 1976 L*a*b* is one of the most widely used object color spaces. In CIELAB, lightness L* is limited between 0 and 100, while a* and b* coordinates have no fixed boundaries. The outer boundaries of CIELAB have been previously calculated using theoretical object spectral reflectance functions and the CIE 1931 and 1964 observers under the CIE standard illuminants D50 and D65. However, natural and manufactured objects reflect light smoothly as opposed to theoretical spectral reflectance functions. Here, data generated from a linear optimization method are analyzed to re-evaluate the outer boundaries of the CIELAB. The color appearance of 99 test color samples under theoretical test spectra has been calculated in the CIELAB using CIE 1931 standard observer. The lightness L* boundary ranged between 6 and 97, redness-greenness a* boundary ranged between −199 and 270, and yellowness-blueness b* boundary ranged between −74 and 161. The boundary in the direction of positive b* (yellowness) was close to the previous findings. While the positive a* (redness) boundary exceeded previously known limits, the negative a* (greenness) and b* (blueness) boundaries were lower than the previously calculated CIELAB boundaries. The boundaries found here are dependent on the color samples used here and the spectral shape of the test light sources. Irregular spectral shapes and more saturated color samples can result in extended boundaries at the expense of computational time and power.     

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.     

Color coordinates of objects with daylight changes

Colorimetric changes were analyzed for a broad set of natural and artificial objects that were illuminated by daylight measured at different solar elevations on separate days, under diverse meteorologic conditions. The changes in L*‐, a*‐, and b*‐color coordinates of the objects, when illuminated with daylight at the maximum solar elevation and at twilight, normally exceeded 3 CIELAB units. However, color differences were not significant when evaluated during the middle hours of the day. Nor were significant differences found in the color of an object on different days, when evaluated during the middle hours. Color appearance attributes of the objects at intervals during the day were also calculated based on the CIECAM97s color appearance model, showing the trends with daylight changes. © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 25–35, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

Experimental object color unique hue data for the mean observer for color appearance modeling

Color appearance models, among other things, predict the hue of a stimulus when compared with defined stimuli that represent the four unique hues. Recent studies have indicated that the stimuli representing with high reliability unique hue (UH) percepts vary widely for different color‐normal observers. The average yellow and blue UH stimuli for 102 observers, as determined in a recent experiment at medium chroma, differ considerably from the CIECAM02 defined unique hues, based on the Swedish NCS. Wide inter‐observer variability precludes color appearance models from accurately predicting, for individual observers, all four unique hue stimuli. However, models should predict accurately those of a well‐defined average observer. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 505–506, 2008     

Colorimetric study of SCOTDIC colour specifier

In this study, SCOTDIC cotton standard colours (a physical exemplification of the Munsell system) were studied extensively. L*, a*, b* values were measured and plotted to check the uniformity of the Munsell (SCOTDIC) hue, value and chroma values in a CIELAB diagram. Although for some borderline hues the hue angles were quite different than expected (around 0° or 360°), the correlation between SCOTDIC hue and CIELAB hue angle was fairly good and the correlation between SCOTDIC value and CIELAB lightness was also quite high. However, the correlation between SCOTDIC chroma and CIELAB chroma was only moderate. In the CIELAB diagram, the constant SCOTDIC hue and constant chroma loci took the shape of approximately linear radial lines starting from the origin and approximately concentric circles with the origin as their centres, respectively. However, some deviations were observed for high chroma colours and yellow hues in the respective cases. The instrumentally predicted Munsell notations were compared with the actual SCOTDIC notations. Some deviations of the SCOTDIC system from the Munsell system were observed.     

A shade guide model based on the color distribution of natural teeth

The objectives were to determine the color distribution of natural teeth sorted by the parameters of Value, Chroma, and hue angle measured with a colorimeter, and to suggest a shade guide model. The color of maxillary and mandibular 12 anterior teeth was measured with a tristimulus colorimeter for 47 subjects (n = 564). The color of teeth was grouped initially by Value (CIE L*) by the interval of 3.3 units. After then, within each main group, the color of teeth was subgrouped by Chroma by the interval of 3.3 units. Chroma was calculated as C*_ab = (a*2 + b*2)^1/2. Since the hue angles were in the first or fourth quadrant, subgroups were further sorted by the first or fourth quadrant hue angles. Hue angle was calculated as h° = arctan (b*/a*). Mean color difference (ΔE*_ab) between the color of an individual tooth and the mean color of each main group was 2.5–3.3, which was lower than acceptable limit (ΔE*_ab < 3.3), and that in each subgroup was 0.9–3.1. The number of subgroups was 22, which was comparable to those of conventional shade guides. A shade guide model based on the color distribution of natural teeth sorted by Value in six main groups, three or four subgroups within each main group sorted by Chroma, and further sorted by hue angle (first or fourth quadrant values) was suggested. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 278–283, 2007     

Unique hue data for colour appearance models. Part I: Loci of unique hues and hue uniformity

Psychophysical experiments were conducted to assess unique hues on a CRT display for a large sample of colour‐normal observers (n = 185). These data were then used to evaluate the most commonly used colour appearance model, CIECAM02, by transforming the CIEXYZ tristimulus values of the unique hues to the CIECAM02 colour appearance attributes, lightness, chroma and hue angle. We report two findings: (1) the hue angles derived from our unique hue data are inconsistent with the commonly used Natural Color System hues that are incorporated in the CIECAM02 model. We argue that our predicted unique hue angles (derived from our large dataset) provide a more reliable standard for colour management applications when the precise specification of these salient colours is important. (2) We test hue uniformity for CIECAM02 in all four unique hues and show significant disagreements for all hues, except for unique red which seems to be invariant under lightness changes. Our dataset is useful to improve the CIECAM02 model as it provides reliable data for benchmarking. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

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     

Precision and accuracy in the color specification of virgin olive oils from the bromthymol blue method

Twenty experienced observers with nondefective color vision judged 27 virgin olive oil samples within an acceptable color range, using the bromthymol blue (BTB) method, under controlled observation conditions (daylight source with a correlated color temperature of 6500 K, and standard gray back-ground). On the average, 44.8% of the observers agreed in their selections of the BTB standard solution matching a given oil sample, and this percentage increased to 88.2% considering ±one step in the two dimensions (pH and concentration) of the BTB scale. On the average, the lowest color difference between oil samples and available BTB solutions was 6.6 Commission Internationale de l'éclairage 1976-(L^*a^*b^*) (CIELAB) units, but this color difference was approximately two times greater for the color difference between oil samples and BTB solutions selected by our observers. The colors of the BTB standard solutions in the CIELAB space are not uniformly distributed, and thus one step in pH or concentration is equivalent to CIELAB color differences varying in a wide range (1.7–13.5 and 1.7–26.3 CIELAB units, respectively). From these values, indicating low precision, accuracy, and uniformity, some suggestions are made for future improvements of the current BTB method.     

Improve neural network‐based color matching of inkjet textile printing by classification with competitive neural network

Nowadays, with increasing use of digital printing in the textile industry, characterization and color matching are very much considered. There is a very complicated relationship between pixel values of input digital image and colorimetric parameters of printed textile samples. One of the most important used methods for inverse characterization of printer and prediction of CMYK digital values is neural network. In this study, the prediction accuracy of CMYK digital values were improved by dividing the training samples into 2, 4, 6, 8, and 10 subgroups using creating a competitive neural network. For classification of samples, L*a*b* or XYZ were introduced to a competitive neural network as input parameters. Then, the classification of test samples was performed by trained competitive neural network. To predict the of CMYK digital values of input digital image, a cascade‐forward back propagation neural network is trained by L*a*b* of each subgroup. The results obtained show that the prediction accuracy of CMYK digital values were improved by suggested method. The best result was obtained by classification of samples with L*a*b* into eight subgroups and using a cascade‐forward back propagation neural network with 4, 4, and 4 neurons in hidden layers.     

A Euclidean color space in high agreement with the CIE94 color difference formula

Many consider it futile to try to create color spaces that are significantly more uniform than the CIELAB space, and, therefore, efforts concentrate on developing estimates of perceived color differences based on non‐Euclidean distances for this color space. A Euclidean color space is presented here, which is derived from the CIELAB by means of a simple adjustment of the a* and b* axes, and in which small Euclidean distances agree to within 10.5% with the non‐Euclidean distances given by the CIE94 formula. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 64–65, 2000     

Different transformation methods between CIELAB coordinates and Munsell hue

This research aims to convert CIE L*C*_abh_ab coordinates into corresponding Munsell hues. Different transformation methods for colour mapping from CIELAB colour space to Munsell hues are proposed. Polynomial equations that predict Munsell hue from CIELAB h_ab suffer from poor performance as there is no direct one‐to‐one mapping. Polynomial methods that predict Munsell hue from all three L*C*_abh_ab values also show limited performance. However, a distance‐weighted look‐up‐table model based upon the CIEDE2000 colour‐difference equation is able to predict Munsell hue to an accuracy of 1 unit of root mean square error. All transformation methods in this paper were developed using CIE illuminant C and the 2° standard observer conditions and were based on 2729 Munsell renotation colour samples.     

Improving the compatibility of disperse dye mixtures using levelling agent — assessment through colour coordinates

The addition of a nonionic levelling agent to a dyebath containing a mixture of three disperse dyes in equal proportions and having similar hues (all in the red—yellow sector of colour space) significantly improved their compatibility, especially at higher applied depths of 3.0% and 4.5%. The dyed samples were measured for the differences in their colour coordinates with respect to the undyed substrate on a spectrophotometer attached to an IBM personal computer. The plots of ΔL* vs ΔC*_ab, ΔL* vs K/S, Δb* vs Δa*, Δa* vs K/S and Δ6* vs K/S clearly indicated the improvement in compatibility of the dye mixture.     

Evaluation of colour‐difference formulae for different colour‐difference magnitudes

Most of the colour‐difference formulae were developed to fit data sets having a limited range of colour‐difference magnitudes. Hence, their performances are uncertain when applying them to a range of colour differences from very small to very large colour differences. This article describes an experiment including three parts according to the colour‐difference magnitudes: large colour difference (LCD), small colour difference (SCD), and threshold colour difference (TCD) corresponding to mean ΔE values of 50.3, 3.5, and 0.6, respectively. Three visual assessment techniques were used: ratio judgement, pair comparison, and threshold for LCD, SCD, and TCD experiments, respectively. Three data sets were used to test six colour‐difference formulae and uniform colour spaces (CIELAB, CIE94, CIEDE2000, CAM02‐SCD, CAM02‐UCS, and CAM02‐LCD). The results showed that all formulae predicted visual results with great accuracy except CIELAB. CIEDE2000 worked effectively for the full range of colour differences, i.e., it performed the best for the TCD and SCD data and reasonably well for the LCD data. The three CIECAM02 based colour spaces gave quite satisfactory performance. © Wiley Periodicals, Inc. Col Res Appl, 2012     

Preprocessing to CIECAM02 input color with chromatic background

A preprocessing to CIECAM02 input color for color appearance prediction was proposed. In this study, 8640 color appearance matching pairs (NCS color charts with red, green, yellow, and blue backgrounds in a light booth and their reproductions with gray background on a CRT screen) were obtained by psychophysical experiment using the simultaneous‐binocular technique. Because only the lightness of background is included in CIECAM02, a color inducing vector based on opponent‐colors theory was introduced to preprocess CIECAM02 inputs, so that CIECAM02 may predict the corresponding color of an input color with chromatic background as well. By data fitting, a color preprocessing formula describing a relationship between the color inducing vector and the NCS chromaticness was conducted. Furthermore, the formula's performance was tested and the results showed that it was good for implementing the color appearance prediction of input colors with different chromatic backgrounds.© 2006 Wiley Periodicals, Inc. Col Res Appl, 32, 40–46, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20287     

Color of natural tooth crown in Japanese people

The aim of this study was to investigate the color of the natural maxillary incisor tooth from Japanese people of all age groups. These results were compared with the Trubyte Bioblend shade guide. The subjects of this study were in the age range of 13–84, 42 male and 45 female making 87 people in total. Areas with 1.0‐mm diameter at five sites were measured along the tooth axis for L*,a*,b*, according to CIELAB color spaces using a Spectroradiometric Color Computer. At the incisal site, two significant positive correlations were found between age and a* (r = 0.376, p < 0.001), and b* (r = 0.483, p < 0.001). At the center site, a significant negative correlation (r = −0.418, p < 0.001) was found between age and L*, but positive correlation (r = 0.497, p < 0.001) was found between age and b*. At the cervical site, a significant negative correlation (r = −0.326, p < 0.01) was found between age and L*, but positive correlation (r = 0.702, p < 0.001) was found between age and b*. Near the root, particularly, the values of a* were greater than those suggested by the Trubyte Bioblend shade guide. In conclusion, as the Trubyte Bioblend shade guide does not match the natural tooth color in red‐green chromaticity near the root, it is significant for us in dentistry to develop new shade guides that match the Japanese people based on the data collected. © 2000 John Wiley & Sons, Inc. Col Res Appl, 25, 43–48, 2000     

Reproducibility of the bromthymol blue standards used for color specification of virgin olive oil

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’éclairage 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).