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     

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     

Color‐difference formula performance for several datasets of small color differences based on visual uncertainty

Visual uncertainty, while reported, is not used routinely when evaluating color‐difference formula performance in comparison with visual data; rather, data are analyzed assuming no uncertainty; that is, repeating the experiment would result in the identical average results. Previously, Shen and Berns developed three methods to determine whether a color‐difference formula was well‐fitting, under‐fitting, or over‐fitting visual data when visual uncertainty was considered, the method dependent on how the uncertainty was reported and the colorimetric sampling of the color‐difference stimuli. The “nonellipsoid standard error method” was used in the current analyses. Three datasets were evaluated: BFD‐P, Leeds, and Witt. For the BFD‐P data, incorporating visual uncertainty led to the same performance results as the average results, that CIEDE2000 was an improvement over CIE94, which was an improvement over CIELAB. For the Witt data, incorporating visual uncertainty led to the same performance results as the average results, that CIEDE2000 and CIE94 had equivalent performance, both an improvement over CIELAB. However, both formulas under‐fitted the visual results; thus, neither formula was optimal. For the Leeds dataset, the visual uncertainty analysis did not support the improvement of CIEDE2000 over CIE94 that occurred when evaluating the average results. Both formulas well fit the visual data. These analyses also provided insight into the tradeoffs between the number of color‐difference pairs and the number of observations when fitting a local contour of equal perceived color difference: In particular, increasing the number of observations was more important than increasing the number of color‐difference pairs. Finally, average standard error could be used to approximate visual uncertainty defined using STRESS. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

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     

Goniocolorimetry: From measurement to representation in the CIELAB color space

The classical definitions of color are well adapted to diffusing objects, whose color is almost independent of the viewing angle, and to very glossy object observed in the specular direction in respect to the light source. For glossy or iridescent objects, the color is difficult to characterize due to its dependence on the viewing direction. In order to cope with such objects and to represent their angle‐dependent colors in a colorimetric space, we adapt the CIELAB space to “goniocolorimetric” measurements. A crucial point when defining this space is the statement of the viewing solid angle. First, we suggest performing a BRDF measurement at high angular resolution in order to characterize the gloss of the specimen. Then, since for the definition of colors the CIE recommends cones of half‐angle of 2° or 10°, we propose to convert the measured BRDF into a reflectance factor defined in respect to these solid angles. This procedure is eased by a planar multispectral image of the BRDF, where solid angles are specified by the pixel size. At last, the reflectance factors are converted into CIELAB coordinates. By using this procedure, the perfect white diffuser but also the perfect mirror can be represented in this colorimetric space. © 2010 Wiley Periodicals, Inc. Col Res Appl, 36, 169–178, 2011;     

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     

Optimization of color reproduction on CRT‐color monitors

In the present experimental study, we quantify the influence of the brightness and contrast levels of a CRT‐color monitor in the color reproduction of 60 Munsell chips distributed throughout the chromatic diagram. The images were captured by two CCD cameras, and the color differences were evaluated after reproducing the chips on a color monitor (the experiment was performed with 3 different monitors) for 9 combinations of brightness‐contrast levels. We evaluated the color differences with 3 different formulas: CIELAB, CIELUV, and CIE94. The results indicate that the optimal settings of a monitor, to minimize the color differences, is a medium or minimum brightness level in combination with a maximum contrast level. This combination ensures a more faithful color reproduction with respect to the original image. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 207–213, 1999     

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.     

Image color-appearance specification through extension of CIELAB

An extension of the CIE 1976 (L*, a*, b*) color space, CIELAB is described for applications in color reproduction. This extension incorporates a more accurate model of chromatic adaptation, capability to distinguish between the modes of appearance of reflective and self-luminous stimuli, and adjustments to account for changes in surround. The extension of CIELAB is referred to as the RLAB color space. This color space can be used for calculating metrics of lightness, chroma, hue, and color difference. It can also be used to determine the required colors for reproduction across changes in media and viewing conditions. A pilot experiment testing the RLAB model for cross-media color reproduction is also described.     

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     

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.     

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     

Iris color and texture: A comparative analysis of real irises,ocular prostheses,and colored contact lenses

In this work, we analyzed the color and texture of irises, ocular prostheses, and cosmetic colored contact lenses measured by means of a multispectral system, which provides the CIE L*a*b* colorimetric coordinates of a high resolution image pixel by pixel. The same subject, who has dark brown irises, participated in the measurement of all the contact lenses. The CIE L*a*b* colorimetric coordinates were analyzed to classify the samples into three major groups (brown, blue and green) using a new algorithm developed for this purpose. This classification allowed us to carry out a comparison of the color associated with each set of samples, using the corresponding color gamuts in the CIE L*a*b* color space. Furthermore, we analyzed the iris color reproduction achieved by prostheses and contact lenses in terms of CIEDE2000 color differences, and obtained closer results with prostheses. In addition, we performed an analysis of texture by means of the color spatial distribution of all samples. This was achieved by means of two statistical approaches: first order statistics of image histograms and second order statistics using co‐occurrence matrices. The results suggest that the texture associated with real irises, ocular prostheses and colored contact lenses is very different. This study provides useful information about the color and texture of irises that may help to establish a strategy for improving the techniques used in the manufacturing process of prostheses and colored contact lenses to obtain a better and more realistic appearance. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

A quantitative network model for color categorization

To clarify the higher‐order mechanism of human color perception, we measured the color appearances of 78 colored lights by an elemental color‐scaling method and by a categorical color naming method. The colors covered nearly the entire CIE 1931 xy‐chromaticity diagram with three different surrounds. The results showed that firm basic color zones derived by categorical color naming can be mapped with no overlap in an opponent‐color response space. We propose a network model with a threshold selector, maximum selectors, and multiplication units with gain factors to generate the categorical color responses quantitatively from the elemental color responses. The model can predict the categorical color naming results in different surround conditions with no change of parameters. This suggests that a nonlinear color vision mechanism for color categorization exists between the primary visual cortex (V1) and the inferior temporal cortex (IT) in the human brain. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 225–232, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10060     

Segmentation of map image using opponent color dimensions

This article describes a method for segmentation of color geographic map images based on color opponency. In this method, a color-map image is transformed into a color opponent representation as proposed for human vision. The color contrast is enhanced in a manner analogous to the opponent surround receptive fields. The enhancement process separates adjacent foreground and background regions into pairs of opponent colors. The segmentation process can then be easily completed based on this opponent structure. © 1996 John Wiley & Sons, Inc.     

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     

A preliminary comparison of CIE color differences to textile color acceptability using average observers

Ninety‐six nylon pairs were prepared, including red, yellow, green, and blue standards, each at two lightness levels with CIE94 ΔE units ranging from 0.15 to 4.01. Visual assessments of acceptability were carried out by 21 females. Logistic regression compared visual results to four color‐difference equations, CIELAB, CMC, CIE94, and CIEDE2000. It was found that CMC most closely represented judgments of average observers. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 288–294, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20124     

Subjective evaluation of uniform color spaces used for color-rendering specification

Three attributes of color appearance of sixteen samples illuminated by various light sources were estimated directly by two observers. The results are plotted on a subjective chroma diagram, with which the psychometricchroma diagrams calculated from the CIELUV and CIELAB spaces are compared. The comparison and numerical evaluations show that the CIELAB space is much better than the CIELUV and U* V* W* spaces in simulating surface-color appearance under various illuminants. Substitution of the CIELAB space for the U* V* W* space used in the CIE method of color-rendering specification is proposed.     

A comparison of constant stimuli and gray‐scale methods of color difference scaling

Two psychophysical techniques, the method of constant stimuli and the gray‐scale comparison method, were used to determine color tolerances for three different color centers in the hue, chroma, and lightness directions in CIELAB color space. The same color‐difference pairs were used as the stimuli in both experiments. Although the results followed the same trends, they were different for the two techniques. Based on comparison of the validity and precision of the results, as well as the ease of implementation, use, and analysis, the method of constant stimuli is the preferable method. © 2002 Wiley Periodicals, Inc. Col Res Appl, 28, 36–44, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.     

An efficient nonlinear polynomial color characterization method based on interrelations of color spaces

A well-known color characterization method is to take an image of a color chart and then to find the mapping matrix from the digital RGBs to the corresponding known CIEXYZs. However, the prediction errors are generally large in CIELAB color space because of the nonlinear transformation from CIEXYZs to CIELABs. In this article, we propose an efficient and simple nonlinear method for the color characterization of input devices. The approach for deriving a colorimetric mapping between digital RGB signals and CIELAB tristimulus values uses the polynomial modeling by considering the interrelations among the standard CIE color spaces. Furthermore, to improve the accuracy of solution, we take the polynomial root terms extension. Our algorithm is simple to implement because only a least-squares mapping should be solved. Various computational results are given to demonstrate the efficiency and capability of the proposed method.