Distance metrics for very large color differences

Small, supra-threshold color differences are typically described with Euclidean distance metrics, or dimension-weighted Euclidean metrics, in color appearance spaces such as CIELAB. This research examines the perception and modeling of very large color differences in the order of 10 CIELAB units or larger, with an aim of describing the salience of color differences between distinct objects in real-world scenes and images. A psychophysical experiment was completed to compare directly large color-difference pairs designed to probe various Euclidean and non-Euclidean distance metrics. The results indicate that very large color differences are best described by HyAB, a combination of a Euclidean metric in hue and chroma with a city-block metric to incorporate lightness differences.     

The development of color metrics

On the occasion of the presentation of the Deane B. Judd-AIC Award to the author, he gave a historical survey of how color metrics developed from its origin to its present state of an independent and fascinating science. Only the most important stages of this development could be discussed; it was difficult to confine the scope of the presentation reasonably. Therefore, it was necessary to consider only those publications and personalities far enough removed in time to allow a retrospective, fair judgment of their importance. Most of the author's living colleagues in color metrics could not be mentioned, but this does not imply any evaluation of their recent publications or research, or deny any important future progress.     

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.     

Spectral color reproduction minimizing spectral and perceptual color differences

In this article, we are combining minimization criteria in the colorant separation process for spectral color reproduction. The colorant separation is performed by inverting a spectral printer model: the spectral Yule‐Nielsen modified Neugebauer model. The inversion of the spectral printer model is an optimization operation in which a criterion is minimized at each iteration. The approach we proposed minimizes a criterion defined by the weighted sum of a spectral difference and a perceptual color difference. The weights can be tuned with a parameter α ∞ [0, 1]. Our goal is to decrease the spectral difference between the original data and its reproduction and also to consider perceptual color difference under different illuminant conditions. In order to find the best α value, we initially compare a pure colorimetric criterion and a pure spectral criterion for the reproduction, then we combine them. We perform four colorant separations: the first separation will minimize the 1976 CIELAB color difference where four illuminants are tested, the second separation will minimize an equally weighted summation of 1976 CIELAB color difference with the four illuminants tested independently, the third colorant separation will minimize a spectral difference, and the fourth colorant separation will combine a weighted sum of a spectral difference and one of the two first colorimetric differences previously introduced. This last colorant separation can be tuned with a parameter in order to emphasize on spectral or colorimetric difference. We use a six colorants printer with artificial inks for our experiments. The prints are simulated by the spectral Yule‐Nielsen modified Neugebauer model. Two groups of data are used for our experiments. The first group describes the data printed by our printing system, which is represented by a regular grid in colorant space of the printer and the second group describes the data which is not originally produced by our printing system but mapped to the spectral printer gamut. The Esser test chart and the Macbeth Color Checker test chart have been selected for the second group. Spectral gamut mapping of this data is carried out before performing colorant separation. Our results show improvement for the colorant separations combining a sum of 1976 CIELAB color difference for a set of illuminants and for the colorant separation combining a sum of 1976 CIELAB color difference and spectral difference, especially in the case of spectral data originally produced by the printer. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 494–504, 2008     

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     

Linearity and additivity of small color differences

The functional relation of visual to colorimetric scaling of small color differences is needed for a realistic interpretation of the perceptual magnitude of a measured color difference. Linearity is usually assumed and differences are expressed in threshold units without adjustment. an experimental plan is described that provides for the application of gray-scale assessment to visual judgments under controlled parameters. Gray scale and test colors were produced from a two-component acrylic lacquer system. A green color center (CIE green) was chosen for a first test with color differences extending from the center in the directions of hue, saturation/chroma, and lightness in steps ranging from -5 to + 5 thresholds. Thirteen observers made 4 judgments of each of 78 color-difference pairs. the resulting scales were typically linear but increasing less steeply than threshold stepping; however, Fstatistics showed some inhomogeneous effects. Scales along the main color directions tended slightly to subadditivity. the vector model of color difference better predicted the magnitude of diagonal jumps between two color directions than did the city-block model. Relations to some recent color-difference formulae were studied and the CIE TCI-29 formula was found to be a good predictor for this color center. © 1995 John Wiley & Sons. Inc.     

Performance testing of color-difference metrics using a color tolerance dataset

A color-difference dataset was developed for testing the performance of color metrics. The dataset comprises 45 color-difference vectors varying in five directions at nine color centers under conditions typical of commercial color decisions. Probit analysis was used to estimate the parameters of the population distribution of tolerances for each vector. In addition to estimating the median tolerance, the anlysis allows one to estimate the uncertainty of a tolerance and to test the adequacy of the underlying model tolerance distribution. The median tolerances were used to specify 45 color-difference pairs with equal visual color-difference magnitudes. The performance of eight color-difference metrics was compared using the normalized standard deviation of the color differences of the visually equal difference pairs as a measure of uniformity. A bootstrap statistical technique was used to quantify the variation in performance with varying samples of color centers and color-difference directions and to determine the significance of observed differences in uniformity performance. Some metrics based on weighted CIELAB dl*, dC*, dH* color-difference components had significantly superior performance compared to the CIE recommended color-difference metrics.     

Individual differences of unique hue loci and their relation to color preferences

Loci of the four unique hues (red, green, blue, and yellow) on the equiluminant plane on the color display and three preferred colors were obtained from 115 normal trichromats. We sought possible correlations between these measures. Different unique hue loci were not correlated with each other. The three preferred colors were not correlated with each other. We found five combinations of significant correlation between a preferred color and unique hue settings, yet the overall tendency is not very clear. We conclude that individual differences in color appearance measured by unique hues and color preferences measured by asking for favorite colors may not be predicted from each other or even within a category because the differences in the earlier visual mechanisms can be compensated for and these high‐level measures can be influenced by learning and experience. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 285–291, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20023     

Refractive index—A key to understanding color differences

A problem encountered in the instrumental evaluation of color is the use of integrating sphere spectrophotometers to compare materials where the standard color plaque is not constructed from the same material as the sample. This problem is encountered in the automobile industry, where polypropylene master plaques are commonly used as the standards constructed from different materials than the sample being evaluated. The paper demonstrates how these problems result from the impact of a polymer's refractive index on the reflectivity of light from its surface, and the effect of this light on the instrumental evaluations of color. The paper also discusses how the Fresnel equation can be utilized to calculate the amount of light reflected from a polymer's surface and describes a mathematical procedure that can help compensate for these differences when matching and approving colors.     

Objective evaluation of color design

For the evaluation of color design, a multiple linear system analogous to the human visual perceptual system was proposed. Using computer-generated random color patterns, a psychological test was performed and its results were analyzed in terms of the Fourier transform of the color patterns. It was clearly concluded that the power spectrum obtained by the Fourier transform of the patterns had characteristic meanings at regions of very-low, low, medium, and high spatial frequencies.     

Color difference predicted by color component differences

A relationship between assessed color differences and assessed components of colors is presented. The perceptual difference between colors j and k is converted to a lightness difference of two Munsell grays, V_A and V_B, and d_jk = |V_A ? V_B|. Scaled values of principal hue components of a color ξ _α(H|V/C), α = R, Y, G, and B, are read from charts based on assessments of observers. Previous charts (Color Research and Application, 1999, 24, 266–279) are enlarged and extended. A linear combination of ΔV = |V_j ? V_k| and Δξ _α = |ξ _α(H_j|V_j/C_j) ? ξ _α(H_k|V_k /C_k)|, d?_jk, is the best to predict d_jk. The root‐mean‐squares of (d_jk ? d?_jk) is 0.34 V, about one third of the lightness difference from V to V + 1 on the Munsell Value Scale.     

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     

Variability in estimation of suprathreshold small color differences

Intraobserver and interobserver variability in perceptual small color difference evaluation are known but are rarely analyzed phenomena. They raise the question if humans are equipped to make reliable and uniform estimates of this kind. Data from two recent well‐controlled studies show that intraobserver and, particularly, interobserver variability are surprisingly large. The use of a gray reference scale in place of a single gray reference pair has a small normative effect on the estimate. A large interobserver variability in the estimated magnitude of chromatic differences compared with achromatic reference pairs remains, with an average ratio between largest and smallest estimate of over 3:1 and ratios for individual observers and sample pairs ranging up to 28:1. No correlation was found between the reliability of judgment and the judged magnitude of difference: highly reliable observer groups are not more consistent. Relative reliability of estimate was found inversely related to magnitude of perceived difference. Consequences of these results for the development of improved color difference formulas are discussed. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 367–374, 2009     

Matching large color differences with achromatic and chromatic surrounds

A color-difference-matching experiment was carried out using a computer-interfaced video-display system. Two reference color stimuli (i) and (j), and one test stimulus (i,j) surrounded by a large achromatic or chromatic stimulus were presented on the video screen. An observer was asked to adjust the test stimulus until he perceived it to have a color precisely “half way” between the colors of the given reference stimuli. A satisfactory half-way color was the one that produced perceptually equal color differences between (i) and (i,j) and (j) and (i,j) and simultaneously made these color differences as small as possible. Although the given color difference between the test stimuli (i) and (j) was generally quite large (100–200 just-perceptible color-difference steps) with the achromatic surround condition, the color-difference matching required to obtain the desired half-way color did not present any serious difficulties to the observer. The uncertainty of the chromaticity point of the half-way color was established by an ellipse of small to moderate size surrounding the mean chromaticity setting. The orientation and shape of the uncertainty ellipse was comparable with the orientation and shape of uncertainty ellipses obtained by other investigators studying color-difference matching involving much smaller color differences. The unexpected and noteworthy result of the present study lies in the location of the chromaticity point of (i,j) relative to those of (i) and (j) as a function of surround color. The location of (i,j) depends on the saturation of the surround stimulus but not its hue. Some speculative explanations of the observed results are offered.     

Visual and instrumental assessments of color differences in automotive coatings

The interest in gonioapparent pigments (metallic, pearlescent, interference, or diffractive) has increased in the last few years, especially for applications in the automotive industry. To assure a proper characterization of colors with gonioapparent pigments, commercial devices have appeared to characterize the color in different geometries, which are called multiangle spectrophotometers. As the gonioapparent pigments and multiangle instruments are relatively new, no studies exist regarding the instrumental‐based procedure followed in the industry, and if the results provided are in agreement with the observer perception. Consequently, the main objective of this study was to examine the correlation of the instrumental color differences with visual assessments. The instrumental color difference was calculated with the color difference formula AUDI2000 (specific for this sector) between the pairs of similar samples of three types of coated panels (solid, metallic, and pearlescent). The values measured by a telespectroradiometer in a directional lighting booth and the colorimetric values obtained by means of a multiangle spectrophotometer BYK‐mac were considered for this purpose. Additionally, a visual experiment was conducted to quantify the color difference by using the gray‐scale method. The results revealed that an acceptable instrumental correlation existed despite the visual and the instrumental correlation being worse. In particular, it was checked that observers accepted a larger number of color pairs, that is, the visual color difference was smaller than the tolerance demanded by the industry (derived from AUDI2000). © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 384–391, 2016     

A hue rectangle and its color metrics in a modified opponent‐colors system

In the proposed modified opponent‐colors system, the hue regular rectangles show the chromatic coordinates of any chromatic colors better than hue circles. In the hue rectangles equihue and equichroma loci are shown together with equigrayness loci. In the color perception space of the modified opponent‐colors system, a city‐block metric must be used instead of a Euclidean one for distance. The reason for this is described in detail. The proposed color perception space constitutes a regular octahedron. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 171–179, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10046     

Request for existing experimental datasets on color differences

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

彩妆品的安全风险评估及控制

彩妆品种类繁多,成分复杂,且多功能彩妆品的出现增加了质量控制成本和产品安全风险。论述了彩妆品的安全风险、物质的评估过程以及彩妆品的安全风险控制。指出对彩妆品危害物质的风险评估应建立在科学的评估程序基础之上,只有树立风险意识和质量意识,才能充分保障化妆品的质量和安全。     

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