A Preliminary Comparison of CIE Color Differences to Textile Color Acceptability Using Average Observers

Transcription

1 A Preliminary Comparison of CIE Color Differences to Textile Color Acceptability Using Average Observers H. Mangine,* K. Jakes, C. Noel Department of Consumer and Textile Sciences, The Ohio State University, Columbus, OH Received 26 April 2003; revised 26 July 2004; accepted 16 January 2005 Abstract: Ninety-six nylon pairs were prepared, including red, yellow, green, and blue standards, each at two lightness levels with CIE94 E units ranging from 0.15 to 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 Wiley Periodicals, Inc. Col Res Appl, 30, , 2005; Published online in Wiley InterScience ( DOI /col Key words: Color difference metrics; color matching; color measurement; color space; CIELAB; CIE94; CMC; CIEDE2000 INTRODUCTION In spite of recent developments in industrial color difference evaluation, color matching in the textile industry still largely depends on visual assessment. 1 Although visual inspection is highly subjective, industry is reluctant to base color-matching decisions wholly on instrumentally measured color differences, since research has yet to show an acceptable level of performance of color difference equations. That is, the level of agreement between visual and instrumental color-matching decisions is low, even with developments like the CMC and CIEDE2000 color difference equations. Although both these newer color difference equations show improvement over CIE L*,a*,b*, results are inconclusive concerning which performs better in agreeing with visual observations, 2,3 with the exception of research involving the datasets on which each equation is based. These datasets are not readily comparable with each other *Correspondence to: Heather Mangine ( mangine.2@osu.edu) 2005 Wiley Periodicals, Inc. because they do not use the same substrate although datasets and substrates were combined in the development of CIEDE If results of research do not show that a real improvement in color difference evaluations is gained in choosing a different equation, companies are unlikely to revise their color-difference metric standards. 5 Various practical applications of CIE L*,a*,b*, which assumes that CIELAB is a uniform color space, have shown the need for weighting factors to predict color differences better. 5 7 CMC, CIE94, and CIEDE2000 each employ such weighting factors to adjust for the inaccuracies. In CMC, lightness is weighted as an increasing function of lightness, so that it is more important at low levels of lightness; 8 however, some researchers argue that it really just reduces the importance of lightness altogether. 6 In addition, chroma is also weighted as a function of itself, and hue is dependent on hue and chroma. 9 CIE94 is similar to CMC, but it is mathematically a simpler equation. With the correction factors, lightness is given a constant weight, chroma is a function of itself, and hue is dependent only on chroma and not on hue. 5,9 In predicting chromatic differences for saturated blues and neutrals, both CMC and CIE94 give large errors. 4 In addition, CIE94 has been validated only for unrealistic and artificial standard reference conditions and color pairs with CIELAB E units less than Literature previous to the development of CIEDE2000 is undecided as to whether CMC or CIE94 performed better, 2,3 while other literature report that CIE94 performs best. 5,11,12 However, CMC is the recommended equation of the AATCC and is widely used throughout the textile industry. Because of the problems associated with the previous equations, the CIE Technical Committee 1 47 was formed. Combining the datasets used to create CMC, CIE94, and other equations, CIEDE2000 was developed. 4 It has the same skeletal formula as other advanced equations, but also 288 COLOR research and application

2 contains an extra correction, R T, that rescales the a* axis 13 to improve E for fitting chromatic differences in the blue region. 4 A few studies have shown that CIEDE2000 performs better than CMC. 14,15 However, questions remain concerning whether it is applicable at all levels of lightness and chroma, and whether the datasets employed in its development are reliable, because this equation is based on combining data derived from datasets that differ in terms of substrate and experimental conditions. 16 Thus far, CIEDE2000 has not shown enough improvement over the other color difference measures to be the recommended equation of the AATCC. In difference measurement it is important to have a high agreement with the average observer. 17 However, the experiments conducted in evaluation of color difference equations did not use average observers, but rather used individuals already familiar with color matching. 3,4,15,18 20 This can affect the results of color experiments. 18 Since these experiments are difficult and time consuming, the observers who were tested are often connected with the lab and may have previous experience, whereas the end users of textile products are ordinary consumers who generally do not have a high degree of experience in color discrimination. With more experienced observers, smaller color discrimination thresholds than normal are obtained. 19 Perhaps color difference equations would have higher correlation with the judgments of average observers, if average observers themselves were used for evaluation of those equations. While it is important that those making color decisions have excellent color discrimination, the use of average observers in color difference studies may lead to an indication of the equation that performs better in simulating the color discrimination of average consumers. Considering the present state of color measurement and color difference equations, the research reported herein was conducted to explore which CIELAB-based color difference equation, CMC(2:1), CIE94(2:1:1), or CIEDE2000(2:1:1), performs best in predicting visual observations of color matches by average observers. A new set of color samples was created and pass/fail judgments were made by average observers. Color differences were then calculated using CIELAB, CMC(2:1), CIE94(2:1:1), and CIEDE2000(2:1:1). Lastly, through statistical measures, conclusions were made concerning which equation and color space more closely matches visual color difference judgments. Preparation of Samples METHOD Dyed standards were prepared with hues that closely corresponded to the hues red, yellow, green, and blue, located very near the hue angles of 0, 90, 180, and 270 in CIELAB color space. For each of these hues, a lighter and a darker standard were prepared by dyeing with 0.2% and 2.0% dye, respectively, on weight of fabric (owf). These gave standards with two lightness levels, one above and one below L* 50, except for the yellow hue, where L* values were above 70 for both levels of dyeing. L*, a*, and b* values of the dyed samples are recorded in Appendix A. A pool of color samples for each standard was created, and color measurements relative to each standard were made using a Datacolor Spectraflash Model SF600 Plus spectrophotometer, 9-mm aperture plate, illuminant D 65, and 10 standard observer. Samples and standards were then paired on the basis of their color difference according to CIE94. For each of the eight color standards, 2 3 samples were chosen that had E* 94 values of less than 1.0 unit, 2 3 samples were chosen that had E* 94 values greater than three units, and the remaining pairs were chosen to show a representative sample of E* 94 values between one and three units. Samples were also chosen to provide variations in hue, chroma, and lightness. Each sample and standard were placed side by side with no apparent separation between them and were framed with neutral gray mat board (L* 65.59, a* 0.83, and b* 3.37), so that a 3 3 area of each fabric was visible. No writing appeared on the front of the card, but they were numbered and labeled 1 96 on the back. Color difference values for CIELAB, CMC (2:1), CIE94 (2:1:1), and CIEDE2000 (2:1:1) were then calculated. Visual Observation of Color Twenty-one female college students with no previous experience in industrial color matching and who passed the Farnsworth-Munsell 100 Hue Test with average or superior color discrimination were used as average observers. Participants came to the lab twice, one week apart at the same time of day. Before each participant s first scheduled experiment, 60 of the 96 color cards were randomly chosen and assigned to a participant. Participants viewed each color set in a MacBeth SpectraLight II color-matching booth under daylight lighting conditions. The rest of the room was dark, 20 so that no other visible light illuminated the samples. Participants were then asked to compare each standard and sample and decide whether they were an acceptable color match to be used in coordinating apparel, like a blazer and a skirt, or in the same piece of apparel, like a bodice and a sleeve. 21 One week later, each participant returned to view the remaining 36 sets as well as 24 additional sets randomly selected from the pool they had already seen. The 60 total sets were presented in random order and the test retest measure ensured reliability of participants (all participants had a correlation of 0.70 or higher). After reliability was checked for each participant, pass/fail data for the second viewing of the 24 reliability samples was excluded from any further analysis to reduce dependency issues. Data Analysis For each of the 96 cards in which the participants either replied zero (does not match) or one (matches), pass-ratios were calculated (Table I). Values ranged from to Volume 30, Number 4, August

3 TABLE I. Pass-ratios, visual data and color difference values for each color pair. Sample pair Pass ratio V CIELAB CMC(2:1) CIE94 (2:1:1) CIEDE2000 (2:1:1) COLOR research and application

4 TABLE I. (Continued) Sample pair Pass ratio V CIELAB CMC(2:1) CIE94 (2:1:1) CIEDE2000 (2:1:1) Since responses for visual observations were binary, logistic regression was chosen to analyze the data. Observational data were converted to V s or visual data using the logit function. To compensate for undefined logit values that stem from pass fail ratios of 0 or 1, the value of 0.5 was added to the numerator and denominator of the logit function. To ensure that all V s are positive, 5 was added to the logit function. 22 The revised logit function appears as follows and results can be found in Table I: V 5 log e S i 0.5 (1) N i S i 0.5 where S i number of observers who found the match acceptable and N i number of observers RESULTS Scatterplots of visual data ( V) versus each of the color difference calculations suggest that the simple linear logistic regression appears to be appropriate for these data (Fig. 1). Logistic regressions for data derived from each of the six color difference equations were run with three different models. Because of the similarity in residual deviance and lack of theoretical reasons to choose another model, a simple linear model was used in which E was run against V. Intercepts, coefficients, and t-values for each of the logistic regressions can be seen in Table II. The calculated t-values showed each to be significantly different from 0, t(96) 1.99, p 0.05, for the intercept and coefficient of each regression. Note that this is not a test of the difference between the four different regression equations. The 2 goodness of fit test (df 95, p 0.05) was then used to assess the ability of each equation to predict color difference equivalent to that of the observed number of participants who declare a given pair as matching (Table III). The CMC equation yields the smallest 2 value, and is therefore the best predictor of visual observations. The ranks of the equations ordered from best to worst fit are CMC, CIE94, CIEDE2000, and CIELAB. DISCUSSION The objective of this study was to find which CIELABbased color difference calculation agrees the most with visual observations of average observers. The 2 goodness of fit, calculated from the logistic regression, indicates that of the CIELAB-based equations, CIELAB fits the visual observations least well, as expected. CIELAB uses the Pythagorean theorem to measure distance within a space. Therefore, it assumes that CIELAB color space is flawless and that lightness and other factors of color each carry equal importance. However, it has already been found that CIELAB does not model color differences well, and therefore other color difference formulae have been proposed. The results of this research concerning CIELAB agree with the literature; weighting functions on the variables of lightness, hue, and chroma are useful to improve the visual uniformity of CIELAB. 5 It has already been shown that CMC, CIE94, and CIEDE2000 correlate with visual observations better than CIELAB does, but this research was designed to determine whether CMC(2:1), CIE94(2:1), or CIEDE2000(2:1) proved to be more representative of the visual discrimination of average observers. Overall, color research is inconclusive as to which performs best. It has been found that CIE94 works well only in standard reference conditions, Volume 30, Number 4, August

5 FIG. 1. Scatterplots of visual data versus color difference calculations. and both the AATCC and ISO recommend CMC as the standard color difference equation, while CIEDE2000 needs more performance testing. In this work, the 2 analysis shows that CMC(2:1) performs better than CIE94(2:1:1) which, in turn, performs better than CIEDE2000(2:1:1). The results of the 2 analysis indicate only the ranking of each equation relative to the others and are inconclusive as to how weighting functions should be formulated. In the weighting function for lightness, CIE94 weights lightness uniformly whereas CMC weights it as a function of lightness; 5 therefore, it would seem that lightness should be weighted as a function of lightness. However, if this were the more appropriate means of weighting, then CIEDE2000, which also weights lightness as a function of itself, 4 should TABLE II. Results of logistic regression for data derived from each color difference equation. Predictor Coefficient Value std. error t-value CIELAB Intercept CMC Intercept CIE Intercept CIEDE Intercept out-perform CIE94. In addition, in CMC and CIEDE2000, the weighting factor for hue is dependent on hue and chroma, whereas in CIE94 this factor is dependent only on chroma. 5,9 However, it is possible that lightness should be weighted as a function of itself rather than being uniformly weighted, and that the weighting factor for hue should be dependent on hue as well as chroma. Possibly, CIEDE2000 performs less well than does CIE94 because of the addition of the R T factor, and its performance is confounded by the conglomeration of data and substrates used in its formulation. The results also show that the use of untrained observers might make a difference in distinguishing the accuracy of the color difference equations in simulating visual observations. While results reported in the literature were either inconclusive or indicated that CIE94 or CIEDE2000 performed better, this research found that when using untrained observers typical of the average consumer, the CMC(2:1) equation is best for evaluation of color differences. Observ- TABLE III. Results of chi-square goodness of fit for each color difference equation. Predictor 2 CIELAB CMC CIE CIEDE COLOR research and application

6 ers have two characteristics that determine whether or not a color pair is an acceptable match. The first characteristic is discrimination. Researchers have found that more experienced observers have smaller discrimination thresholds. 19 Therefore, average observers are less discriminating than experienced colorists. The second characteristic in color acceptability is bias. For example, experienced observers are less willing to indicate a color match since they understand that in real life, negative consequences may occur if the match is not satisfactory to customers. On the other hand, average observers, those without experience in color matching, have biases differing from those of the experienced observer. Consequently, this research indicated that if untrained observers who have average discrimination, and no commercial bias, are employed it is determined that CMC(2:1) is the best equation to use for the evaluation of textile color acceptability. Although CIE94 and CIEDE2000 have been shown to be superior color difference equations when skilled observers are employed, the textile industry is concerned about the judgment of average observers who comprise the end consumer. Therefore, it may be better to use the equation that is most representative of observers who are considered average. CONCLUSIONS In this small exploratory study, it has been shown that when using CIELAB color space, the CMC(2:1) color difference equation performs best; in other words, it yields results most similar to those of observations made by a subset of average observers chosen to represent consumer discrimination. To evaluate more precisely the ability of color difference equations to accurately predict color matches within the textile industry, further research is needed to test the performance of color difference equations. Suggestions for expanding the research initiated in this study include the following: 1. Expand the number of colors used by including a larger variety of hues and lightness levels, and create sample pairs that only differ in one aspect of color, thereby allowing evaluation of observer differences in perception of hue, chorma, and lightness separately. 2. Use sample pairs with smaller color differences. 3. Analyze data using PF/3 analysis, thereby evaluating the degree of disagreement between observers and calculated color differences. 4. Assess acceptability in different light sources. ACKNOWLEDGMENTS Special thanks are due to the observers who took part in the experiment and Datacolor for gifts-in-kind. Financial support was provided by the Mary Lapitsky Graduate Research Endowment and the International Textiles and Apparel Association Eastern Region Fellowship. 1. Steen D, Dupont D. Defining a practical method of ascertaining textile color acceptability. Color Res Appl 2002;27: CMC: Calculation of small color differences for acceptability, AATCC Test Method 173; Qiao Y, Berns RS, Reniff L, Montag E. Visual determination of hue suprathreshold color-difference tolerances. Color Res Appl 1998;23: Luo MR, Cui G, Rigg B. The development of the CIE 2000 colordifference formula: CIEDE2000. Color Res Appl 2001;26: Melgosa M. Testing CIELAB-based color-difference formulas. Color Res Appl 2000;25: Kuehni RG. Why CIELAB needs to be replaced for industrial color difference calculation. Tex Chem Colour 1999;31: McDonald R. European practices and philosophy in industrial colordifference evaluation. Color Res Appl 1990;15: Kuehni RG. Hue scale adjustment derived from the Munsell system. Color Res Appl 1999;24: Kuehni RG. Towards and improved uniform color space. Color Res Appl 1999;24: Griffin LD, Seoehri A. Performance of CIE94 for nonreference conditions. Color Res Appl 2002;27: Guan SS, Luo MR. A color-difference formula for assessing large color differences. Color Res Appl 1999;24: Heggie D, Wardman RH, Luo MR. A comparison of the color differences computed using the CIE94, CMC(l:c) and the BFD(l:c) formulae. J Soc Dyers Colour 1996;112: Cui G, Luo MR, Rigg B, Roesler G, Will K. Uniform color spaces based on the DIN99 color-difference formula. Color Res Appl 2002; 24: Xu H, Yaguchi H, Shiori S. Correlation between visual and colorimetric scales ranging from threshold to large color difference. Color Res Appl 2002;27: Noor K, Hinks D, Laidlaw A, Treadaway G, Harold R. Toward an improved uniform color difference formula. Proceedings of AATCC Color Science Symposium, Raleigh, NC, March 9 10, p Kuehni RG. Communications and comments: CIEDE2000, milestone or final answer? Color Res Appl 2002;27: Kuehni RG. Industrial color difference: Progress and problems. Color Res Appl 1990;15: Alman DH, Berns RS, Snyder GD, Larsen WA. Performance testing of color-difference metrics using a color tolerance dataset. Color Res Appl 1989;14: Perez F, Hita E, del Barco LJ, Nieves JL. Contribution to the experimental review of the colorimetric standard observer. Color Res Appl 1999;24: McDonald R. Industrial pass/fail color matching. Part 1: Preparation of visual color matching data. J Soc Dyers Colour 1980;96: Vanderhoeven RE. Conversion of a visual to an instrumental color matching system: an exploratory approach. Tex Chem Colour 1992; 24: Berns RS. Industrial applications: deriving instrumental tolerances from pass-fail and colorimetric data. Color Res Appl 1996;21: Volume 30, Number 4, August

7 TABLE AI. L*, a*, and b* values for each color pair. Color pair L* a* b* Color pair L* a* b* Color pair L* a* b* 1 standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample standard sample COLOR research and application