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     

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     

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     

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     

Investigating the effect of texture on the performance of color difference formulae

To predict the perceived color differences, the effect of the surface texture on the performance of the color difference formulae was investigated. To this end, knitted polyester fabrics with eight different textures were prepared. The fabrics were dyed by seven dyestuffs in five different depths. The selected pairs from the five samples with different depths in each hue covered small to large color differences. The assessed pair of samples had the same texture and hue, but different depths. A panel of 23 observers assessed the color differences of the pairs by gray scale method. The results showed that for the textile samples with different texture structures, the CIEDE2000 (2:1:1) performed the best followed by CMC (2:1:1), CIE94 (2:1:1), and CIELAB with approximately same performance. In addition, the magnitude of color difference influenced the ability of the formulae to predict the visual assessments and the best performance obtained for medium color differences. The comparison between eight different texture groups indicated that the texture structures of the pairs significantly affected the performance of the color difference formulae. For instance, the PF/3 measures obtained for the eight texture groups by CIEDE2000 (2:1:1) color formula could be varied between 21.98 and 33.37 PF/3 units. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.     

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     

Revisiting the weighting function for lightness in the CIEDE2000 colour-difference formula

We have used 13 experimental datasets (7420 colour pairs) to study the performance of the weighting function for lightness proposed by the CIEDE2000 colour-difference formula, because it has been suggested that this function can be improved by using the weighting function for lightness S_L = 1 adopted by the CIE94 colour-difference formula. Using the standardised residual sum of squares (STRESS) index, it was found that: (i) replacing the S_L in CIEDE2000 with S_L = 1 improved the results for 7/13 datasets considered, but the improvement was statistically significant only for 1/13 datasets; (ii) a Whittle-type lightness-difference formula can be used to replace the term ∆L*/S_L in CIEDE2000, which led to a new colour-difference formula with no statistically significant difference with respect to CIEDE2000 for any of the 13 experimental datasets. A modification of the CIEDE2000 formula using a Whittle-type lightness formula is proposed.     

The new deviate visual functions improving the instrumental estimation on the visual color difference for the metamers with about 1° field size

Although the CIE1931 and 1964 color matching functions have been used in color specification for decades, many researchers, from Allen in 1970 to Hu and Houser in 2006, have found that there still exists a great visual mismatch on the discrimination of color difference as in terms of the CIE color matching functions. Hence, some significant error would be made on color specification due to employing the CIE1931 and 1964 color matching functions. Therefore, six color difference formulae developed from different experimental methods are used to derive various deviate visual functions (DVFs) respectively, and to investigate the effect of these DVFs on the performance of the color difference formulae tested in estimating visual color difference. The results indicate that the performance of the color difference formulae in estimating color difference is significantly improved by the deviate visual functions derived in this study. The CIE94 color difference formula has the best performance in predicting the total visual color difference (ΔV_T) using the DVFs and DVFIIs having the mean values 29 and 27 in PF/4 unit, respectively, while the CMC(l:c) the worst the ones 37 and 38. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 115–127, 2009     

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     

Application of improved back propagation algorithm in color difference detection of fabric

Color is an indispensable indicator of product quality evaluation. To detect the color difference of fabrics, the Levenberg–Marquardt optimized back propagation (BP) algorithm is adopted to extract the color feature values of fabric images. First, RGB values are three inputs of BP neural network, and L*a*b* values measured by spectrophotometer are three outputs of the network. The trained network can obtain the corresponding L*a*b* values conveniently. Then the color difference can be calculated through color difference formula and the characteristic values obtained above. Finally, compared with the color difference calculated by the spectrophotometer, the most appropriate formula can be selected from the four formulas listed in the article (CIEDE2000, CMC, CIE94, and CIELAB) to acquire satisfying results. The experimental results reveal that the color difference of fabrics can be detected with a high accuracy and efficiency with this method. Plenty of duplication workloads and some complex conversion formulas can be avoided, making the acquirement of color difference more efficiently. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 311–317, 2015     

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.     

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     

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     

RIT‐DuPont supra‐threshold color‐tolerance individual color‐difference pair dataset

The RIT‐DuPont dataset has been used extensively for formula development and testing since its inception during the 1980's, for example, in the development of CIE94 and CIEDE2000. The dataset was published as 156 color‐tolerances, T₅₀, along specific vector directions about 19 color centers. Probit analysis was used to transform judgments of 958 color‐difference pairs by 50 observers to these 156 tolerances. For most statistical significance testing, the number of samples determines the confidence limits. Thus, there was an interest in publishing the individual color‐difference pair visual and colorimetric data to improve the precision of significance testing. From these 958 pairs, 828 pairs had determinable visual differences. The others had either excessive visual uncertainty or had unanimous visual judgments such that visual differences were undefined. In addition, a method was devised to assign visual uncertainty to each of these pairs using the principles of maximum likelihood and the T₅₀ values. Comparisons were made between the T₅₀ and individual color‐difference pair data both including and omitting uncertainty weightings. The weighted dataset was found to be equivalent to the T₅₀ tolerances. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2010     

Predictions based on Munsell notation. I. Perceptual color differences

For every pair of colors (j, k), the observers selected a pair of Munsell grays (N_a, N_b) such that the lightness difference matched the color difference in size, and the scaled value of color difference was defined as d_jk = V_a − V_b. On the basis of these data, where (j, k) are limited in the range that can be matched by d_jk < 4.0 V, the procedure was presented to define predicted values d̃_jk for Munsell colors (J, K) between 4V and 7V directly from Euclidean d̂_jk between points P_j and P_k in the current Munsell solid. The procedure is more practical than the multidimensional scaling representation. Inter‐point d̂_jk are measured in the units of C in the (H, C) plane and the contributions d̂_jk of 1C and 1V differences are assumed to be 1 and 2.3. Precision of predictions, RMS = {mean of d_jk − d̃_nk)²}^0.5, is 0.3 V (0.8 C) for 2‐D color differences (V_j = V_k). For the set of data on 3‐D color differences used in the present study, RMS = 0.6 V (1.7 C). These were compared with the precision of predictions by Judd, Adams–Nickerson formulae, CIE 1976(L*, u*, v*), and CIE94. © 1999 John Wiley & Sons, Inc. Col Res Appl, 24, 10–18, 1999     

Euclidization of the first quadrant of the CIEDE2000 color difference system for the calculation of large color differences

The calculation of colour distances in the first quadrant of the CIEDE2000 space can be realized now after the author succeeded in working out such calculations in the CIE94 and CMC space in preceeding articles. The new system is presented and then the Euclidean line element is established, from which terms are derived for the new coordinates of lightness, hue, and hue angle. The calculations of colour distances are carried out with the new Euclidean coordinates according to a well‐known method and are demonstrated by examples guided by CIE94 and CMC distances from the preceeding articles. Finally, proposals are given for the eventual improvement of the CIEDE2000 formula. © 2005 Wiley Periodicals, Inc. Col Res Appl, 31, 5–12, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20168     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

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     

The perception of static colored noise: Detection and masking described by CIE94

We present psychophysical data on the perception of static colored noise. In our experiments, we use the CIE94 color difference formula to quantify the noise strength and for describing our threshold data. In Experiment 1 we measure the visual detection thresholds for fixed pattern noise on a uniform background color. The noise was present in one of three perceptual color dimensions lightness (L*), chroma (C*), or hue (h). Results show that the average detection threshold for noise in L* is independent of hue angle and significantly lower than that for noise in C* or h. Thresholds for noise in C* and h depend on hue angle in an opponent fashion. The measured detection thresholds, expressed in terms of the components ΔL*/k_LS_L, ΔC*/k_CS_C, and ΔH*/k_HS_H that build up the CIE94 color difference formula are used to tune CIE94 to our experimental conditions by adjusting the parametric scaling factors k_L, k_C, and k_H. In Experiment 2, we measure thresholds for recognizing the orientation (left, right, up, down) of a test symbol that was incremental in L*, C*, or h, masked by supra‐threshold background noise levels in L*, C*, or h. On the basis of the CIE94 color difference formula we hypothesized (a) a constant ratio between recognition threshold and noise level when the test symbol and background noise are in the same perceptual dimension, and (b) a constant recognition threshold when in different dimensions. The first hypothesis was confirmed for each color dimension, the second however, was only confirmed for background noise in L*. The L*, C*, h recognition thresholds increase with increasing background noise in C* or h. On the basis of some 16,200 visual observations we conclude that the three perceptual dimensions L*, C*, and h require different scaling factors (hue dependent for C* and h) in the CIE94 color difference formula, to predict detection threshold data for color noise. In addition these dimensions are not independent for symbol recognition in color noise. © 2008 Wiley Periodicals, Inc. Col Res Appl, 33, 178–191, 2008