Color errors of digital cameras

The mean color errors of a high‐quality digital camera are defined in CIELAB and CIEDE2000 ΔE units by using 16 ceramic color samples, whose accurate CIELAB values have been measured by a calibrated spectrophotometer. The bandwidths of CCD's color filters are evaluated by taking photographs of CRT‐display primaries. The lowest mean color errors were 13.1 CIELAB ΔE units and 8.1 CIEDE2000 ΔE units before corrections. Large color errors are decreased successfully by using three different methods: simple photoeditor, gamma correction, and multiple regression. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 217–221, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20007     

Visual evaluation at scale of threshold to suprathreshold color difference

Visual evaluation experiments of color discrimination threshold and suprathreshold color‐difference comparison were carried out using CRT colors based on the psychophysical methods of interleaved staircase and constant stimuli, respectively. A large set of experimental data was generated ranged from threshold to large suprathreshold color difference at the five CIE color centers. The visual data were analyzed in detail for every observer at each visual scale to show the effect of color‐difference magnitude on the observer precision. The chromaticity ellipses from this study were compared with four previous published data, of CRT colors by Cui and Luo, and of surface colors by RIT‐DuPont, Cheung and Rigg, and Guan and Luo, to report the reproducibility of this kind of experiment using CRT colors and the variations between CRT and surface data, respectively. The present threshold data were also compared against the different suprathreshold data to show the effect of color‐difference scales. The visual results were further used to test the three advance color‐difference formulae, CMC, CIE94, and CIEDE2000, together with the basic CIELAB equation. In their original forms or with optimized K_L values, the CIEDE2000 outperformed others, followed by CMC, and with the CIELAB and CIE94 the poorest for predicting the combined dataset of all color centers in the present study. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 198–208, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20106     

Assessing the color of red wine like a taster's eye

Color of 33 commercial red wines and five‐color reference wines was measured in the same conditions in which visual color assessment is done by wine tasters. Measurements were performed in the two distinctive regions, center and rim, which are the regions assessed by wine tasters when the wine sampler is tilted. Commercial wines were classified into five color categories using the color specifications in their taste cards. The five color categories describe the spread of red hues found in red wines from the violet to brown nuances. The performance of CIELAB color coordinates in terms of their ability to reproduce the observed classification has been established using discriminant analysis. The CIELAB hue angle, h_ab, measured in the rim, where wine thickness is of the order of few millimeters, gives the best results classifying correctly 71.1% of the samples. Classification results are not significantly improved when additional color coordinates are considered. Moreover, ΔE* color differences with color reference wines do not provide good classification results. The analysis of reference and commercial wines supports the fact that hue is the main factor in the classification done by wine tasters. This is reinforced by the linear correlation found between h_ab in the rim and the wine age (R² = 0.795) in accordance with the fact that wines change their hues from violet to brown tints with ageing. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 153–162, 2009     

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     

Development of a comprehensive visual dataset based on a CIE blue color center: Assessment of color difference formulae using various statistical methods

The objectives of this work were to develop a comprehensive visual dataset around one CIE blue color center, NCSU‐B1, and to use the new dataset to test the performance of the major color difference formulae in this region of color space based on various statistical methods. The dataset comprised of 66 dyed polyester fabrics with small color differences ($\Delta E_{{\rm ab}}^* < 5$ ) around a CIE blue color center. The visual difference between each sample and the color center was assessed by 26 observers in three separate sittings using a modified AATCC gray scale and a total of 5148 assessments were obtained. The performance of CIELAB, CIE94, CMC(l:c), BFD(l:c), and CIEDE2000 (K_L:K_C:K_H) color difference formulae based on the blue dataset was evaluated at various K_L (or l) values using PF/3, conventional correlation coefficient (r), Spearman rank correlation coefficient (ρ) and the STRESS function. The optimum range for K_L (or l) was found to be 1–1.3 based on PF/3, 1.4–1.7 based on r, and 1–1.4 based on STRESS, and in these ranges the performances of CIEDE2000, CMC, BFD and CIE94 were not statistically different at the 95% confidence level. At K_L (or l) = 1, the performance of CIEDE2000 was statistically improved compared to CMC, CIE94 and CIELAB. Also, for NCSU‐B1, the difference in the performance of CMC (2:1) from the performance of CMC (1:1) was statistically insignificant at 95% confidence. The same result was obtained when the performance of all the weighted color difference formulae were compared for K_L (or l) 1 versus 2. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2011     

Color memory in elderly adults

The methods of simultaneous and successive color matching have been compared for a set of five Munsell color samples by 50 older adult observers, 25 men and 25 women (ranging in age from 64 to 80 years). From comparison between this population and one of 50 younger adult observers, 25 men and 25 women (in the 20–27 age range), we can deduce, in general, the following: (a) In the elderly adults the mean CIELAB total color difference (ΔE*_ab) in simultaneous color matching is lower than the ΔE*_ab by memory color matching. (b) While younger adults matched well the color of all the reference tests, the elderly adults matched poorly both greens and orange. (c) Younger adults remember the original color better than do the older adults (P = 0.007), depending on gender and delay time. (d) Although with simultaneous matching, the observer's gender does not determine significant differences, by memory, men matched the color of reference test more poorly than did women (P = 0.0), independently of age, color and delay time, especially for bluish green, violet, and pink. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 458–467, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20258     

Color memory matching: Time effect and other factors

The methods of simultaneous and successive, or memory, color matching have been compared for 10 color reference samples distributed in two groups each performed by 50 observers (25 men and 25 women). Our results, obtained with a total of two hundred Munsell color chips arrayed on ten gray cardboard panels, indicate that: (a) while by simultaneous matching the mean color differences obtained are, in most cases, lower than 1 CIELAB unit, those obtained by memory are generally higher; (b) the worst remembered colors are yellow, light green, blue, and pink, and the best remembered color is orange; (c) the influence of the delay time (15 s, 15 min, and 24 h) is significant for the remembered mean color (p < 0.03); (d) we find significant men-women differences for the remembered mean color (p < 0.05). © 1998 John Wiley & Sons, Inc. Col Res Appl, 23, 234–247, 1998     

Which color is more colorful,the lighter one or the darker one?

To answer a question often asked in industrial color reproduction, a series of highly chromatic color samples of the same CIELAB hue but of small variations of CIELAB chroma and lightness were prepared and scaled for perceived colorfulness. The results indicate that lightness contributes to the perceived colorfulness as defined by the observers according to their everyday color experiences. For the samples used, colorfulness can be modeled by factoring in the CIELAB L* value in addition to CIELAB C*. The results show that colorfulness, as implied in our everyday color experiences, can be a complex perceptual attribute. A newer psychophysical scaling model is also presented, since Thurstone's Case V model was shown to be inadequate. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 168–174, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10142     

Prediction of optical efficacy of vital tooth bleaching using regression analysis

Establishing a colorimetric guideline to predict the effectiveness of tooth bleaching could produce a more reliable dental treatment. The purpose of this study was to evaluate the effectiveness of tooth bleaching and to test the predictability of tooth color changes. A 10% carbamide peroxide bleaching system was used in studies at Harvard University and at Iwate Medical University in Japan. L*, a*, and b* values (CIELAB) for pre‐ and postbleaching were obtained and color differences (ΔE) were calculated. The b* and L* values of the original tooth color indicated a relatively strong to moderate correlation with ΔE values, whereas a* showed a weak correlation. The multiple‐regression equation obtained from the color data of Harvard subjects performed better than the predictive model. The predicted ΔE correlated strongly with the observed ΔE (r = 0.78). The validation of this equation on data collected from Iwate confirmed the strong correlation (r = 0.74). © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 390–394, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20048     

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     

Correlation between visual and colorimetric scales ranging from threshold to large color difference

Psychophysical experiments of color discrimination threshold and suprathreshold color‐difference comparison were carried out with CRT‐generated stimuli using the interleaved staircase and constant stimuli methods, respectively. The experimental results ranged from small (including threshold) to large color difference at the five CIE color centers, which were satisfactorily described by chromaticity ellipses as equal color‐difference contours in the CIELAB space. The comparisons of visual and colorimetric scales in CIELAB unit and threshold unit indicated that the colorimetric magnitudes typically were linear with the visual ones, though with different proportions in individual directions or color centers. In addition, color difference was generally underestimated by the Euclidean distance in the CIELAB space, whereas colorimetric magnitude was perceptually underestimated for threshold unit, implying the present color system is not a really linear uniform space. Furthermore, visual data were used to test the CIELAB‐based color‐difference formulas. In their original forms CIEDE2000 performed a little better than CMC, followed by CIELAB, and with CIE94 showing the worst performance for the combined data set under the viewing condition in this study. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 349–359, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10081     

Performance of two color scales for virgin olive oils: Influence of ripeness,variety, and harvest season

The purpose of this investigation was to compare the performance of the bromthymol blue (BTB) method and the Uniform Oil Color Scale (UOCS) method with different sets of virgin olive oil samples from Andalusia (Spain), namely, 1213 samples from olives at three stages of ripeness, 1008 samples from eight olive varieties, and 1700 samples from olives harvested in four different crop seasons. All oils were extracted in the laboratory by the same procedure. The performance of the two color scales was compared using CIELAB color differences between the oil samples and the nearest standard from each scale. The UOCS performed at least 2.0 times better than the BTB for each of the three stages of olive ripeness, and the difference between the two color scales was statistically significant (P<0.001). The UOCS performed at least 1.3 times better than the BTB for each of the eight olive varieties studied, and the differences between the two color scales were statistically significant (P<0.02). The UOCS also performed at least 1.6 times better than the BTB for each of the four harvests analyzed, and the differences between the two color scales were statistically significant (P<0.001). Using the current oil samples, we could discern no substantial improvements to the UOCS standards.     

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     

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.     

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     

Color evolution during a coating process of pharmaceutical tablet cores by random spraying

Placebo white tablet cores (lactose anhydrous [47.6%], corn starch [23.8%], microcrystalline cellulose [19.1%], polyvinylpyrrolidone [7.9%], magnesium stearate [0.8%], and talcum powder [0.8%]) were coated with a colorant (hydroxypropyl methylcellulose [8% w/v], titanium dioxide [0.2% w/v], FD&C yellow No. 6 with aluminum lacquer [0.8% w/v], polyethylene glycol 4000 [0.4% w/v], and purified water [q.s.p. 100 mL]) using a random spraying method during 130 minutes. During the coating process, batches of 21 samples were extracted every 10 minutes and measured with a DigiEye imaging system. The initial cores showed very similar and uniform colors (Mean Color Difference from the Mean [MCDM] of 0.8 CIELAB units), but partially coated tablets showed lower uniformity (MCDM below 2.0 CIELAB units). There was a high color variability (MCDM about 4.0 CIELAB units) among tablets of the same batch in the period between 10 and 30 minutes, which decreased as the coating process progressed, until achieving a final acceptable value (MCDM below 2.0 CIELAB units). During the coating process, L^* decreased, C^*_ab strongly increased, and h _ab remained nearly constant (disregarding results at 0 and 10 minutes). CIELAB and CIEDE2000 color differences (mainly chroma differences) with respect to the initial color of the tablets were modeled as a function of time by exponential functions with three coefficients. The color change in the interval from 90 to 130 minutes (4.3 CIELAB units, or 2.6 CIEDE2000 units), may be considered negligible bearing in mind the color variability in the batches of 21 samples and typical values of visual color thresholds.     

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     

Observer variability in metameric color matches using color reproduction media

Standard color-matching functions are designed to represent the mean color-matching response of the population of human observers with normal color vision. When using these functions, two questions arise. Are they an accurate representation of the population? And what is the uncertainty in color-match predictions? To address these questions in the dual context of human visual performance and cross-media reproduction, a color-matching experiment was undertaken in which twenty observers made matches between seven different colors presented in reflective and transmissive color reproduction media and a CRT display viewed through an optical apparatus that produced a simple split-field stimulus. In addition, a single observer repeated the experiment 20 times to estimate intra-observer variability. The results were used to evaluate the accuracy of three sets of color-matching functions, to quantify the magnitude of observer variability, and to compare intra- and inter-observer variability in color-matching. These results are compared with various techniques designed to predict the range of color mismatches. The magnitude of observer variability in this experiment also provides a quantitative estimate of the limit of cross-media color reproduction accuracy that need not be exceeded. On average, the differences between matches made by two different observers was approximately 2.5 CIELAB units. © 1997 John Wiley & Sons, Inc. Col Res Appl, 22, 174–188, 1997