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.     

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.     

Study on metamerism degree evaluation based on wavelength sensitive cone weighting algorithm

Metamerism phenomenon can be used in illuminant detection to ensure the accuracy of light source. A method based on Long‐, Middle‐, Short‐ wavelength cones(LMS) weighting algorithm to evaluate metamerism degree is proposed. The chromatic relationship between the degree of metamerism mismatch and the light source is studied. Herein, the consistency between the metameric indices (MIs) and CIE1976 L*a*b* color difference ranking is analyzed by SRCC, KRCC, PLCC and RMSE. A statistically sampling method to obtain practical LMS cone fundamentals to evaluate metamerism degree is employed. The analysis results obtained show that the method based on LMS weighting algorithm has good evaluation ability and stability in simulation experiments and statistically sampling experiments, which are in line with visual characteristics of human. Proposed method meets the requirements of selecting metameric pairs used in light source detection. The analysis results have certain guiding significance.     

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017     

Dependence of the color appearance of some flowers on illumination

Seven flower colors perceived by five color experts using visual color measurement under 2800 K warm white fluorescent lamps, 3500 K plant growth lamps, and 6500 K light‐emitting diodes (LEDs) were compared with those under 6500 K fluorescent lamps, which represented illuminants in florist shops. Fluorescent lamps (6500 K, 1000 lx) were found to be effective for displaying flower colors and were used as the standard condition. The colors of flowers generally shifted in the same direction as those of the illuminants in CIELAB space. The color differences were highest under the 3500 K fluorescent lamp at both 500 and 2000 lx. At 500 lx, the ΔE values under the 6500 K LED were higher than those under the 2800 K lamp. The C* and ΔE values revealed that the 2800 K lamp was unsatisfactory for purple‐blue and purple flowers and was more suitable for floral displays at lower illuminance. Under the 3500 K lamp, the highest color distortion occurred in cool‐colored flowers, but C* increased for purple‐blue and purple flowers. The 6500 K LED tended to decrease C* for warm‐colored flowers under both illuminances, but it was effective for displaying purple‐blue and purple flowers with increased C*. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 28–36, 2014     

Image quality evaluation for high dynamic range and wide color gamut applications using visual spatial processing of color differences

High dynamic range (HDR) and wide color gamut imagery has an established video ecosystem, spanning image capture to encoding and display. This drives the need for evaluating how image quality is affected by the multitudes of ecosystem parameters. The simplest quality metrics evaluate color differences on a pixel‐by‐pixel basis. In this article, we evaluate a series of these color difference metrics on four HDR and three standard dynamic range publicly available distortion databases consisting of natural images and subjective scores. We compare the performance of the well‐established CIE L*a*b* metrics (ΔE₀₀ , ΔE₉₄ ) alongside two HDR‐specific metrics (ΔE_Z [J_za_zb_z], ΔE_ITP [IC_TC_P]) and a spatial CIE L*a*b* extension (). We also present a novel spatial extension to ΔE_ITP derived by optimizing the opponent color contrast sensitivity functions. We observe that this advanced metric, , outperforms the other color difference metrics, and we quantify the improved performance with the steps of metric advancement.     

Color preference affected by mode of color appearance

Most color preference research focuses on colors in an object color mode. In our daily life, however, colors are perceived not only as an object color mode but also as other modes, such as unnatural object color and light source color modes. To explore the effect of the color appearance mode on color preference, we examined the relationship between color preference and the mode of color appearance. Thirty‐three color chips were chosen from the Munsell notation varying in hues and chromas. The color chips were presented in different color appearance modes by changing the subject's room illuminance and the color chip room illuminance. The experimental results showed that the brightest and most saturated colors were preferred. It was found that the subject preferred color in a light source color mode and unnatural object color mode to color in an object color mode. Moreover, we found that hue had a small effect on color preference in the light source color mode. We also investigated the relationship between color preference and the perceived color attributes (perceived chromaticness, whiteness, and blackness). In a supplementary experiment, elementary color naming was conducted. The results showed that the perceived chromaticness, perceived whiteness, and perceived blackness play a role for the determination of color preference for different color appearance modes. We, consequently, suggest that color preference is dominated not only by color attributes but also by the mode of color appearance. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2010     

Research on the detection of fabric color difference based on T‐S fuzzy neural network

T‐S fuzzy neural network algorithm is used to establish the mapping relationship from the RGB space to the L*a*b* space, which avoids the complex process of color space conversion. Meanwhile, the block method is adopted to detect color difference of dyed fabric that is wide format and wide viewing angle. Color differences in different regions can be calculated with Color Measurement Committee color difference formula based on T‐S fuzzy neural network. Experimental results are in accordance with the spectrophotometer measurement, which proves that T‐S fuzzy neural network algorithm used in real‐time color detection process is effective and feasible. Workers can make corresponding adjustment on‐line according to the deviation to ensure the quality of fabric color and reduce the loss.     

Categorical color constancy under RGB‐LED light sources

The light‐emitting diode (LED)‐based light sources have been widely applied across numerous industries and in everyday practical uses. Recently, the LED‐based light source consisting of red, green and blue LEDs with narrow spectral bands (RGB‐LED) has been a more preferred illumination source than the common white phosphor LED and other traditional broadband light sources because the RGB‐LED can create many types of illumination color. The color rendering index of the RGB‐LED, however, is considerably lower compared to the traditional broadband light sources and the multi‐band LED light source (MB‐LED), which is composed of several LEDs and can accurately simulate daylight illuminants. Considering 3 relatively narrow spectral bands of the RGB‐LED light source, the color constancy, which is referred to as the ability of the human visual system to attenuate influences of illumination color change and hold the perception of a surface color constant, may be worse under the RGB‐LED light source than under the traditional broadband light sources or under the MB‐LED. In this study, we investigated categorical color constancy using a color naming method with real Munsell color chips under illumination changes from neutral to red, green, blue, and yellow illuminations. The neutral and 4 chromatic illuminants were produced by the RGB‐LED light source. A modified use of the color constancy index, which describes a centroid shift of each color category, was introduced to evaluate the color constancy performance. The results revealed that categorical color constancy under the 4 chromatic illuminants held relatively well, except for the red, brown, orange, and yellow color categories under the blue illumination and the orange color category under the yellow illumination. Furthermore, the categorical color constancy under red and green illuminations was better than the categorical color constancy under blue and yellow illuminations. The results indicate that a color constancy mechanism in the visual system functions in color categories when the illuminant emits an insufficient spectrum to render the colors of reflecting surfaces accurately. However, it is not recommended to use the RGB‐LED light source to produce blue and yellow illuminations because of the poor color constancy.     

Texture affects color emotion

Several studies have recorded color emotions in subjects viewing uniform color (UC) samples. We conduct an experiment to measure and model how these color emotions change when texture is added to the color samples. Using a computer monitor, our subjects arrange samples along four scales: warm–cool, masculine–feminine, hard–soft, and heavy–light. Three sample types of increasing visual complexity are used: UC, grayscale textures, and color textures (CTs). To assess the intraobserver variability, the experiment is repeated after 1 week. Our results show that texture fully determines the responses on the Hard‐Soft scale, and plays a role of decreasing weight for the masculine–feminine, heavy–light, and warm–cool scales. Using some 25,000 observer responses, we derive color emotion functions that predict the group‐averaged scale responses from the samples' color and texture parameters. For UC samples, the accuracy of our functions is significantly higher (average R² = 0.88) than that of previously reported functions applied to our data. The functions derived for CT samples have an accuracy of R² = 0.80. We conclude that when textured samples are used in color emotion studies, the psychological responses may be strongly affected by texture. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010     

Color change due to surface state modification

The dependence of color on the surface state of an object is calculated in the context of spectrophotometric measurements in a back‐scattering configuration. A modification of the surface roughness leads to a vertical shift of the reflectance spectrum. This translation is related, on a physical basis, to the relevant characteristic of the topography: the ratio h/l (ie, r.m.s. roughness/surface correlation length). Changes in L*a*b* colorimetric coordinates associated with this translation are computed. Finally, the color change ΔE is related to the surface state modification via the ratio h/l. Computations show that the color becomes lighter and less saturated when the surface becomes rougher. The color change is more important for dark or saturated initial objects, and also more important for a yellow surface than for a blue one. Finally, the minimum roughness modification that can induce a visible color change is determined. These results could be applied for industrial needs (quality control), or in the artistic field of conservation or restoration (to follow the color of paintings). © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 45–49, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

What can we learn from a dress with ambiguous colors?

We performed objective spectroradiometric measurements on an LCD image of the recently famous Tumblr dress which is typically perceived by people as blue/black or white/gold. The average ± standard deviation of the CIELAB coordinates was as follows: For a set of 33 points in the areas considered as blue/white, L* = 46 ± 6, C*_ab = 33 ± 6, and h_ab = 282 ± 3°, and for a set of 36 points in the areas considered as black/gold, L* = 29 ± 6; C*_ab = 10 ± 4; h_ab = 16 ± 34°. Initially, this first set of values has low variability and corresponds to a blue color, whereas the second set of values has a very large hue‐angle range, including points which can be considered as both gold and black colors. We also performed spectrophotometric measurements on an original model of this dress, and, assuming D65 illuminant and CIE 1931 colorimetric standard observer, the average results were L* = 26, C*_ab = 39, and h_ab = 289°, and L* = 10, C*_ab = 1, and h_ab = 290° for the blue/white and black/gold points, respectively. We discuss the influence of different factors on the blue/black and white/gold perceptions of different people, including observers' variability in color‐matching functions, Bezold–Brücke and Abney effects, background influence, and illumination assumptions. Although more research on the effect shown in this dress is needed, we think that from this example we can learn that objects do not have specific colors; that is, color is a human perception, and many times the answer of the human visual system is not simple and relies on assumptions of unknown, and variable, origin. © 2015 The Authors Color Research & Application Published by Wiley Periodicals, Inc., 40, 525–529, 2015     

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     

Color effect of light sources on peridot based on CIE1976 L*a*b* color system and round RGB diagram system

Previous research indicated that the peridot's color is dominated by the selective absorption of visible light caused by ferrous ion, the hue angle of which is in an inverse ratio of the concentration of Fe²⁺. This article focuses on the color effect of peridot under different standard light sources based on the CIE1976 L*a*b* color space system and round RGB diagram system and tries to find the best one for its grading and display. Based on the results of a series of experiments, including electron microprobe analysis, spectrophotometer, UV‐Vis spectrum, standard illumination box, and Munsell neutral color chips, it was suggested that the spectral power distribution and color temperature of a standard light source significantly influence the color of peridot in terms of lightness and chroma, particularly in the hue of peridot. As for color grading and displaying of peridot, standard light source A fails to fit in, and the color of peridot under a fluorescent light source has a higher chroma but a lower hue angle than that under daylight light source. The best choice for grading and displaying peridot is the standard light source D₆₅. It is better to distinguish the hue of peridot when it is calculated by the round RGB diagram system.     

Color stability of denture shade tabs is affected by exposure to daylight and decontamination protocols

The purpose of this study was to investigate the effect of light exposure and decontamination protocols on the color stability of denture shade guide tabs. Fifty tabs for shades 62, 66, and 69 (Biotone IPN, Dentsply Sirona) were submitted to baseline L*a*b* measurements (EasyShade, Vita), separated into 5 experimental groups (n = 10), and subjected to one of the following conditions: G1–distilled water (DW‐H₂O)–control; G2 ?70% alcohol; G3–sodium hypochlorite 1% (NaClO); G4–no light exposure; G5–natural light exposure for 6 months. The experimental conditions were designed to simulate 6 months of clinical use. After the test period, final color measurements were recorded. The mean tristimulus coordinate difference (ΔL*,Δa*,Δb*) and total color difference values () were analyzed using 2‐way ANOVA and the Tukey test, α = .05. G2 (alcohol) produced less (P < .05) color change in shade 69 than G3 (NaClO). G5 (light exposure) affected the color stability for all shades, producing a statistical difference (P < .05) from G4 (no light exposure). It was concluded that natural light changes the color stability of the shade guides and that decontamination with 70% alcohol had the least impact on the color stability of the shade guide tabs.     

Analysis of five sets of color difference data

In a systematic optimization process five sets of recent color difference data have been analyzed for commonalities. Adjustment of the X tristimulus values and application of a systematic, surround dependent S_L function was found to be beneficial in all cases. Other modifications of the CIE94 color‐difference formula were found to bring improvements only in some cases and may be spurious. Application of what seem to be nonsystematic scale factors in a range of 0.78–1.38 improve correlation between calculated and visual color differences in all cases. After optimization, calculated color difference values explain between 80–90% of the variation in visual color differences. Some of the datasets are shown not to be well suited for formula optimization. Optimization in all cases by set, for three sets of data by quadrant in the a^*b^* diagram, and for one set by subset did not reveal any additional systematic trends for improvement. It appears that the basic structure of CIE94, with the recommended modifications, is a good approximation as a model for color‐difference evaluation in the range from 0.5–10 units of difference. The model is surround dependent. A number of issues remain to be resolved. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 141–150, 2001     

Unique hue judgments using saturated and desaturated Munsell samples under different light sources

Past studies investigating the unique hues only used samples with a relatively high saturation levels under standard illuminants. In this study, 10 observers selected the four samples with unique hues from 40 V6C8 (Value 6 Chroma 8) and 40 V8C4 (Value 8 Chroma 4) Munsell samples under six light sources, comprising three levels of D_uv (i.e., 0, ?0.02, and ?0.04) and two levels of correlated color temperature (i.e., 2700 and 3500 K). Significant differences were found between the two chroma levels for unique blue and yellow, with the hue angles of unique yellow and blue judged using the desaturated samples being significantly different from those defined in CIECAM02. The iso‐lines of unique yellow, blue, and green did not always go through the origin of the a*‐b* or a′‐b′ planes in CIELAB and CAM02‐UCS. Thus, the problems of CIECAM02, CIELAB, and CAM02‐UCS identified in this study need further investigations.     

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     

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