Color prediction models for digital Jacquard woven fabrics

The current industry practice for producing jacquard fabrics uses computer‐aided design (CAD) systems that provide visual simulations of the final color appearance of actual fabrics prior to production. This digital process is fundamentally based on the prediction of combined weave‐color effects, which can be successfully achieved by accurate color mixing models and the structural details of the fabrics. With the accurate models used in CAD systems, designers would see simulations more closely resembling fabrics to be produced. By checking the previews, the designers can easily modify, that is, recolor, the designs on the display monitor without doing repetitive physical sampling with the adjustment of the weaves and the yarn colors. However, there is no ready applicable accurate color mixing model for woven structures and there has not been sufficient investigation of the color prediction despite its usefulness for the current digital CAD process. Our study investigated the, color prediction of jacquard woven fabrics designed based on the principle of optically subtractive color mixing with the use of CMY colors. The color prediction was firstly done through the application of the six color mixing models previously developed for various other applications including fiber blending and printing. The performance of each model was evaluated by calculating the difference between the predicted and the measured colorimetric data, using ΔE_CMC(2:1). The average color difference from the models was 11.93 ΔE_CMC(2:1), which is hardly acceptable in textile industry. In order to increase the accuracy in color prediction, the six models were then optimized. As a result, substantial improvements for all models were obtained with a decrease in color difference to 4.83 ΔE_CMC(2:1) on average after the optimizations. Among the six optimized color mixing models, the optimized Warburton‐Oliver model, that is, W‐O model, was found to have the lowest average ΔE_CMC(2:1) value of approximately equaling to 2, which is considered potentially useful to be applied to the current digital fabric color prediction. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 64–71, 2016     

Color appearance modeling of bicolor striped woven fabrics considering neighboring color effects

In this article, the effect of the spatial and colorimetric attributes of neighboring color on color appearance shift in bicolor striped woven fabrics is investigated. A total of 240 test/neighboring woven color combinations were constructed in four different striped paradigms. Each test color in the combinations was visually assessed by 12 observer panels with the use of the magnitude estimation method estimating the magnitude of perceptual color attributes lightness, colorfulness, and hue. The visual estimates obtained were analyzed statistically by employing correlation and simple regression methods, and, as a result, the following significant neighboring color effects were detected and individually defined: (1) neighboring color's size, lightness, colorfulness, and hue on test color's lightness, (2) neighboring color's colorfulness and hue on test color's colorfulness, and (3) neighboring color's hue on test color's hue. Furthermore, through multiple regression analysis, color appearance models by which the lightness, colorfulness, and hue of bicolor woven fabrics can be predicted were derived. The predictive performance of the models was evaluated by calculating the difference between the visually estimated and the predicted color appearances, using ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1). Among all the derived models, the model producing the smallest mean error was chosen as a final model, and its great accuracy in color appearance predictions was verified through further statistical evaluation. It is envisaged that the findings of this research are of benefit to design textile products with bicolor striped woven fabrics to have desired color appearances. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 512–521, 2017     

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     

Do repeated firings affect the color properties of porcelain‐fused‐metal restorations that are fabricated differently?

Repeated firings can affect the quality of the porcelain color. The purpose of this study is to evaluate the effect of repeated firings on the color changes of porcelain‐fused‐metal restorations that are manufactured using different methods. A total of 60 cylindrical shaped cobalt–chromium alloys (Ø = 10 mm and h = 1.5 mm) were fabricated using casting (C), milling (M), direct metal laser sintering with and without annealing (EL+, EL‐), and selective laser melting with and without annealing (CL+, CL‐). The samples were veneered with A2 (as indicated by the Vita Shade Guide) dentin porcelain of 2 mm thickness. Then the samples subjected to the repeated firings (2nd, 4th, 6th, 8th, and 10th), and the color of each sample was recorded using a spectrophotometer. The CIEDE2000 (ΔE₀₀) formula was used to calculate color differences of the samples on repeated firings. Repeated measures analysis of variance (ANOVA ) and post‐hoc Tukey's test were utilized to analyze the results (a = 05). The L*, a*, and b* values of porcelain‐fused‐metal specimens were significantly affected by the number of firings (P < 0.001) and fabrication techniques (P < 0.001). The ΔE₀₀ values for C, M, CL‐, and EL‐ groups after 10th firing were above 0.8 unit, which indicates that visually perceivable color differences are clinically acceptable. On the other hand, the ΔE₀₀ values for CL+ and EL+ groups were above the PT value after 8th repeated firings. The color properties of porcelain‐fused‐metal restorations were affected by the fabrication techniques and the number of firings.     

Towards automation of color/weave selection in Jacquard design: Model verification

Jacquard woven fabrics are made from colored yarns and different weaves for designing complex pictorial and other patterning effects. The final visualized color effect is the result of assigning weave designs to different areas of the pattern to be created. The current practice in creating Jacquard woven fabric designs is to produce many samples in a trial‐and‐error attempt to match artwork colors. An ability to simulate accurately the appearance of a design prior to manufacture is highly desirable to reduce trial‐and‐error sample production. No automated accurate digital color methodology is yet available to assist designers in matching the patterned woven fabric to the desired artwork. To achieve this, we developed a geometrical model to predict the color contribution of each yarn on the face of the fabric. The geometrical model combined with a Kubelka‐Munk based color mixing model allowed the prediction of the reflectance properties of the final color for a given design. We compared the predicted and experimental values of the reflectance properties for a range of fabrics using the same geometric model with three separate color mixing models. The geometrical model combined with a log‐based color mixing model produced reasonable agreement between predicted and measured ΔE_ab, with an average ΔE_ab of approximately five. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 225–232, 2009     

Prediction of optical efficacy of vital tooth bleaching using regression analysis

Establishing a colorimetric guideline to predict the effectiveness of tooth bleaching could produce a more reliable dental treatment. The purpose of this study was to evaluate the effectiveness of tooth bleaching and to test the predictability of tooth color changes. A 10% carbamide peroxide bleaching system was used in studies at Harvard University and at Iwate Medical University in Japan. L*, a*, and b* values (CIELAB) for pre‐ and postbleaching were obtained and color differences (ΔE) were calculated. The b* and L* values of the original tooth color indicated a relatively strong to moderate correlation with ΔE values, whereas a* showed a weak correlation. The multiple‐regression equation obtained from the color data of Harvard subjects performed better than the predictive model. The predicted ΔE correlated strongly with the observed ΔE (r = 0.78). The validation of this equation on data collected from Iwate confirmed the strong correlation (r = 0.74). © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 390–394, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20048     

Influence of repeated firings on the color parameters of two different all‐ceramic materials

All‐ceramics materials have been widely used in dental practice due to advantages of esthetic outcome. Color parameters, one of the major factors for the esthetic outcome, are affected from different factors such as repeated firings, chemical composition and thickness. In clinical practice, ceramics were frequently subjected to the repeated firings, but the effects of the repeated firings on the color parameters have been underestimated or unknown, so the aim of the study was to evaluate the effects of the repeated firings on the color parameters of all‐ceramic materials. Two commercially available A2 shaded all‐ceramic systems were used in this study (lithium disilicate pressable ceramics and computer aided design/computer aided manufacturing [CAD/CAM] fabricated zirconia). Ten samples for each group (zirconia and lithium disilicate) were prepared and subjected to repeated firings, respectively (1st, 3rd, 5th). Color measurements were performed after each repeated firings using a colorimeter (Minolta CR 321, Konica Minolta, Tokyo, Japan). The perceptibility threshold and acceptability threshold for color changes (ΔE₀₀) was defined as 0.8 and 1.8, respectively. Statistical analysis of the results was performed by using repeated measures ANOVA for color parameters and using non‐parametric Kruskal‐Wallis and Mann‐Whitney‐U test for ΔE₀₀ values (P < .05). L*, a* values showed statistically significant differences for lithium disilicate pressable ceramics and were not significant the zirconia specimens. ΔE₀₀ values were above the perceptibility level for both lithium disilicate and zirconia specimens. Repeated firings affect the color parameters of the lithium disilicate specimens. As a result of repeated firings, lithium disilicate ceramics become lighter and greener.     

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     

Effect of tertiary amines on yellowing of UV‐curable epoxide resins

The work reported demonstrates that the yellowness of UV‐curable epoxide resins can be improved by adding certain tertiary amines in appropriately determined amounts. According to the results of our experiments, 2.0 wt% benzoyl peroxide added to a resin effectively enhances the crosslinking density, and phenolic free radicals are produced during UV curing, which consequently induce yellowness via the reaction of oxygen and the free radicals. Imidazole (1‐amine) and tertiary amines, including 1,2‐dimethylimidazole (2‐amine), 2,4,6‐tris(dimethylaminomethyl)phenol (3‐amine), 1‐methylimidazole (4‐amine) and 2‐methylimidazole (5‐amine), were chosen to be added to resins, and their effects on UV conversion and yellowness were investigated. According to the experimental results, tertiary amines in the resin can provide a certain degree of improvement in yellowness index (ΔYI) and color parameter (ΔE*_ab) of the resin sample. Whatever the type of tertiary amine, it is found that the optimum content of amine in resin is 1.0 wt%. Also, among the studied amines, the 3‐amine exhibits the highest UV reactivity and the best efficiency for yellowness improvement with values of Δa*, Δb*, ΔYI and ΔE*_ab as low as ? 1.4, 6.23, 11.27 and 6.48, respectively. Copyright © 2007 Society of Chemical Industry     

A fallacy in the definition of ΔH*

When a color differs from the reference, it is desirable to ascribe the difference to differences in the perceptual attributes of hue, chroma, and/or lightness through psychometric correlates of these attributes. To this end, the CIE has recommended the quantity ΔH* as a psychometric correlate of hue as defined by ΔH* = [(ΔE*)² - (ΔL*)² - (ΔC*)²]^1/2, where the correlates correspond to either the 1976 CIELAB or CIELUV color spaces. Since ΔH* is defined as a “leftover,” this definition is valid only to the extent that ΔE* comprises exclusively ΔL*, ΔC*, and ΔH* and that ΔL*, ΔC*, and ΔH* are mutually independent compositionally, both psychophysically and psychometrically. It will be shown that as now defined ΔH* lacks psychometric independence of chroma and always leads to incorrect hue difference determination. Such a deficiency causes problems, especially in the halftone color printing industry, since it can suggest an incorrect adjustment for the hue of the inks. A revised definition herein of ΔH* provides a psychometric hue difference independent of chroma, valid for large and small psychometric color differences regardless of chroma. However, for small chromas, the seldom used metric ΔC might be a better color difference metric than ΔH* because complex appearance effects make the perceptual discrimination of lightness, chroma, and hue components more difficult than for high chromas.     

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     

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.     

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     

Color distribution of maxillary primary incisors in Korean children

The purpose of this study was to investigate the color distribution of maxillary primary incisors measured with a colorimeter. The subjects were 100 Korean children aged 2–5 with total number of 400 teeth. A spot measurement intraoral colorimeter was used to determine the color of maxillary primary central and lateral incisors at labial central area. The CIE L*, a*, b* value of each tooth and color difference (ΔE) among each other were calculated and analyzed. The range of L*, a*, and b* values, regardless of the type of teeth, was 72.7–84.9, ?0.6 to 4.9, and 4.7–15.0, respectively. Mean value (SD) of L*, a*, and b* for maxillary primary incisors was 78.6 (2.3), 1.2 (0.9), and 9.6 (1.8), respectively. Boys showed more red (higher a* value) and less yellow (lower b* value) hue than girls in the central incisors (P < 0.05). Mean color difference (ΔE) (SD) between two values which selected from overall 400 L*, a*, b* values measured (n = ₄₀₀C₂) was 3.9 (1.8) with 95% confidence interval range of 3.86–3.89, and most of them were found to be present around the previously reported clinical acceptability thresholds (ΔE = 2.7–6.8). Because mean intraperson ΔE (SD) was 3.0 (1.6) with 95% confidence interval range of 2.86–3.12, most colors among primary incisors in the same person were presumably difficult to discern by naked eye (ΔE < 3.7). Age influenced L* and b* values significantly, but the correlation coefficients were not high (r = ?0.182 for L* of central incisors, P < 0.01; r = 0.188 for b* of central incisors, P < 0.01; and r = 0.143 for b* of lateral incisors, P < 0.05). The present study showed somewhat higher color coordinates than the previous reports which based on primary anterior teeth in other ethnic groups. The results of this study could be used for the color modification of esthetic materials for primary teeth. © 2009 Wiley Periodicals, Inc., Col Res Appl, 2010.     

Use of a predictive colour model for managing the colour appearance of two‐colour woven fabrics

The aim of this study was to implement a two‐dimensional colour appearance model for prediction of the colour values of weft threads when the optical mixing of a two‐colour woven structure had to match the colour appearance of a single‐colour reference woven fabric. Five single‐colour woven fabrics were woven from five threads of similar hue. One of the samples was chosen as a reference, for which the colour appearance was the goal to be achieved in the two‐colour woven fabrics prepared with the other available warp threads and newly dyed weft threads. The colour values of dyed weft threads were predicted by a two‐dimensional colour appearance model. With dyed weft threads, managing the colour appearance of the two‐colour woven fabric was enabled to achieve the colour values of the reference. In the results, colour deviations between the predicted and measured colour values of weft threads revealed some limitations to the colour appearance model and performance of the dyeing process. After the production of the two‐colour woven fabric, the colour appearance matched the appearance of the reference, resulting in deviations of ΔE_CMC(2:1) = 1.2‐7.8. Moreover, the differences between theoretically predicted and measured colour values of the two‐colour woven fabric were evaluated as small, ranging from ΔE_CMC(2:1) = 1.5‐1.9. The results demonstrated the efficiency of implementing the colour appearance model and the dyeing process of weft threads as an approach to achieve the defined colour appearance of two‐colour woven fabrics, which with small colour deviations matches the colour of a single‐colour reference.     

Dependence of the color appearance of some flowers on illumination

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

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.     

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 evaluation of wool fabric dyed with Rhizoma coptidis extract

Berberine is the only cationic colorant of natural plant dyes, which lies in the roots of Rhizoma coptidis and stems of phellodendron. In this study, wool fabric was dyed with the extracts of R. coptidis. Color evaluation was characterized with ΔE, L*, a*, b*, c*, H⁰, K/S. Effects of mordant, extraction concentration, pH value of dye bath, and treatment temperature on color values were studied. Results indicated that wool fabrics dyed with mordant, or at higher temperature, or in alkali solution possessed deeper shades and darker colors. And the wool fabric showed good antibacterial property after dyeing with R. coptidis extracts. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 3376–3380, 2006     

3D Master Toothguide according to L*, C*, and h* coordinates

The purposes of this study are to describe the color coordinates of the 26 shade tabs of the 3D Master Toothguide according to their L*, C*, h* coordinates, and to calculate ΔE_ab*, ΔL*, ΔC*, and Δh* in the 26 shade tabs. The tooth color of 1361 maxillary central incisors was measured “in vivo” using a spectrophotometer. Tooth color was recorded following the 3D Master system and its corresponding L*, C*, h*color coordinates. Of the 325 shade tabs pairs compared a color difference ( ) of between 2.6 and 5.5 units was found in 9.54% (31 pairs). In 291 pairs (89.54%) there was color difference that exceeded the 5.5 units. Only 0.92% of the color differences () were less than 2.6 units. The minimum color difference ( ) found was 2.1 units and the maximum 45.0 units. The intervals in lightness, chroma, and hue groups between adjacent shade tabs were not uniform. In conclusion, this toothguide is clearly ordered regarding lightness or the L* coordinate. Most of the color differences between the 26 shade tabs of the 3D Master exceed the perceptible threshold of 2.6 units. Some clinical implications are as follows: the 26 shade tabs of the 3D Master Toothguide offer improvements as far as spatial arrangement is concerned. Thus, it is highly possible that the subjective visual color of the measurement would be correct. If there is a mistake when choosing the shade tab from the 3D Master guide there will quite probably be an unacceptable color difference from the clinical point of view. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 518–524, 2015.