Investigation for the color samples with equi-depth in Natural Color System

The color depth, an important attribute of color, can reflect the amount of dye partly, which has important functions on the evaluation of color-fastness and strength of dyes, dyeing effect of fabric, computer color matching, and so on. Natural Color System, an internationally accepted color system, orders colors by three parameters (blackness, chromaticness, and hue). The color depth has not been specified within the Natural Color System. This article tries to find the regularity of the sample with equal-depth in Natural Color System. Firstly, 1950 color samples in Natural Color System were measured using an X-Rite Color i7 Spectrophotometer, and their color depths were calculated by five color depth formulas. Then, trend analysis and mathematical modeling methods were used to achieve the connection between the color depth and the notations of Natural Color System basing on color depth theories. Results show that, in Natural Color System, the color samples with the same distance to pure white do not have equal depth; but the color samples with the same nuance (equal blackness, whiteness and chromaticness) have broadly equal color depth, and their average coefficient values are lower than that of Society of Dyers and Colourists Standard Depths. Besides, regressive formulas were built, with which the color depths of any chips in Natural Color System can be calculated broadly by their notation.     

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     

聚酯纤维织物颜色增深方法及色深评价方法

聚酯纤维以其独特的性能在纺织领域大量使用,但由于纤维表面的高反射光量使得织物的得色较浅。汇总介绍了聚酯纤维织物的颜色增深方法,包括复合纺丝、表面处理、改善染整工艺条件、纤维截面异形化以及添加增深剂等,还介绍了目前用于表征织物色深的主客观评价方法和色深的计算公式,分析了常用表观色深评价公式的优缺点及适用范围,以期实现色深主客观评价的一致性。     

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.     

Time‐temperature superposition principle application to the hygrothermal discoloration of colored high‐density polypropylene/wood composites

Discoloration kinetics of wood plastic composites (WPCs) made of recycled high density polyethylene (HDPE) during accelerated hygrothermal aging test at 45–65°C was conducted by means of time‐temperature superposition principle (TTSP). Color parameters measured with a spectrophotometer by CIELAB color system showed that L* and ΔE* increased while a* and b* decreased with aging time and temperature. A single horizontal shift was adequate for the superposition of color parameter master curves, indicating temperature can be used as accelerating factor to the discoloration of the studied WPCs. The regression line for Arrhenius plot showed a linearity for color parameters (R² = 0.9996), suggesting TTSP concept can be used to analyze and predict the discoloration kinetics of WPCs at low aging temperature. The prediction of color parameters at ambient aging temperature (21°C) can be extended to 7.2 years. The activation energy calculated from color parameters using TTSP method was 85.5 kJ/mol and similar to the reported values of HDPE thermal relaxation reactions calculated from other methods in the open literature. POLYM. COMPOS. 37:1016–1020, 2016. © 2014 Society of Plastics Engineers     

Research on the detection of fabric color difference based on T‐S fuzzy neural network

T‐S fuzzy neural network algorithm is used to establish the mapping relationship from the RGB space to the L*a*b* space, which avoids the complex process of color space conversion. Meanwhile, the block method is adopted to detect color difference of dyed fabric that is wide format and wide viewing angle. Color differences in different regions can be calculated with Color Measurement Committee color difference formula based on T‐S fuzzy neural network. Experimental results are in accordance with the spectrophotometer measurement, which proves that T‐S fuzzy neural network algorithm used in real‐time color detection process is effective and feasible. Workers can make corresponding adjustment on‐line according to the deviation to ensure the quality of fabric color and reduce the loss.     

Further investigation on the modified hyperbolic function in the CAM16 color appearance model

In the International Commission on Illumination (CIE) color appearance model CIECAM02, a modified hyperbolic function is used to represent luminance adaptation. The same nonlinear function is also used in the new color appearance model CAM16 [Color Res Appl., 2017;42:703‐718]. Although the modified hyperbolic function seems reasonable based on physiological evidence, it has an infinite slope at the origin, which causes instability for both the forward and inverse modes of the CIECAM02/CAM16 models. In this article, various possible extensions to the nonlinear luminance adaptation function in CIECAM02/CAM16 are reviewed and evaluated. Based on these investigations, the Gill extension to the hyperbolic function that is used to represent luminance adaptation [Proceedings of 16th Color and Imaging Conference, pp. 327‐331, 2008], is recommended at both the lower end (q < q_L ) and the upper end (q > q_U ), where q is the appropriate R_c , G_c , B_c (or R_wc , G_wc , B_wc ) response. In addition, the new recommended function can be readily inverted for use in the appropriate inverse appearance model. From an extensive analysis using available experimental data sets, we also propose that, for the lower and upper limits of the luminance range in the extended model, the values q_L = 0.26 and q_U = 150 be used, respectively.     

Color characteristics of Beijing's regional woody vegetation based on Natural Color System

Seasonality is a typical characteristic of Beijing's regional vegetation, and plant color is one of the most prominent visual factors of vegetation dynamic. In this research, we explored the composition and dynamic characteristics of plant color in Beijing's urban vegetation, involving the analysis of overall characteristics and respective features of leaf, flower, and fruit colors. Color data was collected from 177 woody plant species in Beijing Botanical Garden, spanning their annual life cycle, and identified with the colorimetry of the Natural Color System (NCS). Correlation and regression analyses were applied to reveal the temporal dynamic features of overall plant color richness. Cluster analysis was applied to categorize tree species based on typical colors of various plant organs. Color richness and color dispersion were introduced as two factors to measure color diversity of various tree species, applied in species evaluation by sorting and principal component analysis (PCA). Color dispersion of three‐dimensional NCS data was measured with a modified SD based on the calculation of mean spatial distance in the NCS space. Main results are as follows. The first part is plant color composition. The composition of all plant colors contains 862 NCS color species, 20 blackness species ranging from 3 to 90, 20 chromaticness species ranging from 0 to 90, 35 hue species ranging from G10Y‐B90G, and N. The second part is temporal dynamic of overall color richness. Leaf color richness and total color richness are significantly positively correlated with pentad (5‐day) sequence; flower color richness is significantly negatively correlated with pentad sequence; and fruit color richness first increases and then decreases over time. The third part is cluster analysis of tree species. Based on typical growing‐leaf color, various tree species were clustered into 6 categories; based on typical senescent‐leaf color, various tree species were clustered into 6 categories; based on typical flower color, various tree species were clustered into 15 categories; based on typical fruit color, various tree species were clustered into 7 categories. The fourth part is color diversity evaluation of various tree species with PCA. According to the PCA of flower‐leaf color diversity, the species with higher leaf color diversity and higher flower color diversity include Cotinus coggygria, Lagerstroemia indica, and Amygdalus triloba; the species with higher flower color diversity and lower leaf color diversity include Campsis radicans and Tamarix chinensis; the species with higher leaf color diversity and lower flower color diversity include Acer ginnala and Crataegus pinnatifida; the species with lower color diversity both for flower and leaf colors include Fontanesia fortune and Gleditsia sinensis. According to the PCA of leaf color diversity, the species with higher leaf color diversity in both leaf growth period and leaf senescence period include Diospyros kaki, Lagerstroemia indica and Paeonia suffruticosa; the species with higher leaf color diversity in leaf growth period and lower leaf color diversity in leaf senescence period include Amygdalus persica ‘Atropurpurea’ and Prunus virginiana ‘Canada Red’; the species with higher leaf color diversity in leaf senescent period and lower color diversity in leaf growth period include Quercus palustris, Armeniaca sibirica, and Metasequoia glyptostroboides; the species with lower leaf color diversity for the whole leaf development period include Gleditsia sinensis and Swida walteri.     

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     

Repeatability,reproducibility, and accuracy of a novel pushbroom hyperspectral system

This study analyzes the repeatability, reproducibility and accuracy of a new hyperspectral system based on a pushbroom sensor as a means of measuring spectral features and color of materials and objects. The hyperspectral system consisted of a CCD camera, a spectrograph and an objective lens. An additional linear moving system allowed the mechanical scanning of the complete scene. A uniform overhead luminaire with daylight configuration was used to irradiate the scene using d:45 geometry. We followed the guidelines of the ASTM E2214‐08 Standard Practice for Specifying and Verifying the Performance of Color‐Measuring Instruments that define the standards and latest multidimensional procedures. The results obtained are analyzed in‐depth and compared to those recently reported by other authors for spectrophotometers and multispectral systems. It can be concluded that hyperspectral systems are reliable and can be used in the industry to perform spectral and color readings with a high spatial resolution. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 549–558, 2014     

Comparison of the metrics between the CIELAB and the DIN99 uniform color spaces using dental resin composite material values

The sizes for the perceptible or acceptable color difference measured with instruments vary by factors such as instrument, material, and color‐difference formula. To compensate for disagreement of the CIELAB color difference (ΔE^*_ab) with the human observer, the CIEDE2000 formula was developed. However, since this formula has no uniform color space (UCS), DIN99 UCS may be an alternative UCS at present. The purpose of this study was to determine the correlation between the CIELAB UCS and DIN99 UCS using dental resin composites. Changes and correlations in color coordinates (CIE L*,a*, and b* versus L₉₉, a₉₉, and b₉₉ from DIN99) and color differences (ΔE^*_ab and ΔE₉₉) of dental resin composites after polymerization and thermocycling were determined. After transformation into DIN99 formula, the a value (red–green parameter) shifted to higher values, and the span of distribution was maintained after transformation. However, the span of distribution of b values (yellow–blue parameter) was reduced. Although color differences with the two formulas were correlated after polymerization and thermocycling (r = 0.77 and 0.68, respectively), the color coordinates and color differences with DIN99 were significantly different from those with CIELAB. New UCS (DIN99) was different from the present CIELAB UCS with respect to color coordinates (a and b) and color difference. Adaptation of a more observer‐response relevant uniform color space should be considered after visual confirmation with dental esthetic materials. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 168–173, 2006     

A test of uniformities in the OSA‐UCS and the NCS

In the OSA‐UCS (Optical Society of America–Uniform Color Scales), except for colors on the boundary of the three‐dimensional solid (L, j, g), each color is surrounded by the 12 nearest neighboring colors that are supposed to be perceptually equally different (local uniformity). In the Swedish NCS (Natural Color System), colors are arranged so as to gradually vary in each of the three perceptual attributes: hue, ?; blackness, s; and chromaticness, c. The gradual change in an attribute may correspond to change of color difference from one to the next with a constant step (local uniformity). In each of these color‐order systems, the uniformity was tested by a color‐difference formula d? based on color‐component differences. When a coordinate is fixed (e.g., j in OSA‐UCS, or c in NCS), d? for neighboring pairs turned out fairly constant. However, systematic differences were found between d? in one coordinate and d? in another coordinate. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 277–283, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10162     

Color harmony: New applications of existing concepts

There is a continuing need in the arts for practical applications of color theories. Many existing color-harmony theories can be grouped into two categories: those based on complementary color relationships and those based on a common color denominator. Publication of Ellen Marx's book, Optical Color and Simultaneity, makes new applications of complementary harmony possible, while the existence of the OSA Uniform Color Scales samples makes possible new applications of common denominator as well as complementary color theories.     

Effect of orange fiber addition on yogurt color during fermentation and cold storage

Orange fiber obtained from orange juice by‐products was added to yogurt. Fiber (0%, 0.6%, 0.8%, and 1% doses and different fiber size: 0.417–0.701 and 0.701–0.991 mm) effects on color during yogurt fermentation and cold storage were studied. Overall composition, pH, acidity, syneresis, L*, a*, and b* values were determined. Sensory evaluation of yogurts was carried out. Fiber addition did not cause changes in yogurt acidification and color during fermentation process, though decreased L* value and increased b* value of the milk. Color evaluation along fermentation is pH dependent (R > 0.870). pH decreased and syneresis increased along cold storage. Because of the acidification process, L* value decreased and a* and b* values increased in all yogurts. Yogurts with 1% fiber were significantly different from the others along cold storage, presenting lower L*, higher a* and b* values, and lower syneresis. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 457–463, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20158     

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     

Chromaticity‐matched but spectrally different light source effects on simple and complex color judgments

As light‐emitting diode (LED) light sources mature, lighting designers will be able to deliver white light with a variety of spectral power distributions and a variety of color rendering properties. This experiment examined the effects of three spectral power distributions (SPDs) that were matched in illuminance and chromaticity on three measures of color perception: one objective (performance on the Farnsworth‐Munsell 100 hue test) and two subjective (judgments of the attractiveness of one's own skin, and preferences for the saturation of printed images). The three SPDs were a quartz‐halogen (QH) lamp and two LED sources that were matched to the QH lamp in terms of both illuminance and chromaticity; the three light sources were nominally CCT = 3500 K, x = 0.40, y = 0.39 and ～ 400 lx. LED A used three channels (red, green, blue), and had very poor color rendering (R_a = 18). LED B used four channels (red, amber, cyan, white) and had very good color rendering (R_a = 96, whereas the QH had R_a = 98). Secondary hypotheses addressed the effects of age and skin and eye color on the dependent measures. As expected, LED A delivered very different color perceptions on all measures when compared to QH; LED B did not differ from QH. The results show that it is possible for LED sources to match the familiar incandescent sources. However, although it is possible to deliver what appear to be millions of colors with a three‐chip (RGB) device, there is the risk of creating a very poor luminous environment. © 2013 National Research Council Canada and Wiley Periodicals, Inc. Col Res Appl, 39, 263–274, 2014; Published Online 12 April 2013 in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/col.21811     

Assessing the color of red wine like a taster's eye

Color of 33 commercial red wines and five‐color reference wines was measured in the same conditions in which visual color assessment is done by wine tasters. Measurements were performed in the two distinctive regions, center and rim, which are the regions assessed by wine tasters when the wine sampler is tilted. Commercial wines were classified into five color categories using the color specifications in their taste cards. The five color categories describe the spread of red hues found in red wines from the violet to brown nuances. The performance of CIELAB color coordinates in terms of their ability to reproduce the observed classification has been established using discriminant analysis. The CIELAB hue angle, h_ab, measured in the rim, where wine thickness is of the order of few millimeters, gives the best results classifying correctly 71.1% of the samples. Classification results are not significantly improved when additional color coordinates are considered. Moreover, ΔE* color differences with color reference wines do not provide good classification results. The analysis of reference and commercial wines supports the fact that hue is the main factor in the classification done by wine tasters. This is reinforced by the linear correlation found between h_ab in the rim and the wine age (R² = 0.795) in accordance with the fact that wines change their hues from violet to brown tints with ageing. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 153–162, 2009     

Correspondence Between CIELAB and CIELUV Color Differences

The CIE presently recommends two uniform color spaces, the CIE 1976 (L*u*v*)-space (CIELUV) and the CIE 1976 (L*a*b*)-space (CIELAB). With each of these spaces is associated a color-difference formula. Color differences calculated by one formula cannot readily be converted to color differences calculated by the other formula. A conversion factor such as ρ = ΔE_uv*/ΔE_ab* cannot be determined uniquely. However, for any given location in color space, it is possible to determine a range, ρ_min ? ρ ? ρ_max, within which ρ must lie. Lines of constant ρ_min and ρ_max can be plotted in (L*a*b*)-space which indicate the range of ρ in (L*u*v*)-space.     

彩色涤纶短纤维色差的控制

介绍了用于评定色差的CIELAB和CMC色差公式,并对2个公式在彩色涤纶短纤维色差控制中的实际运用做了对比实验。从对比结果和数据分析来看,CIELAB色差公式存在一定的局限性,CMC色差公式测试结果与目测具有更好的视觉一致性,完全可以在纺织行业色差控制上替代CIELAB色差公式并推广使用。     

Theory of corresponding colors as complementary sets

A corollary of Grassman's linearity law is formally derived, and states: If a number of colors have a corresponding color appearance A in different illuminants, then their complementary colors have a corresponding color appearance B. The informal logic is that: (1) a perceived color has only one complementary color; (2) two or more corresponding colors have the same complementary color (given the illuminant whites are a color match); so (3) the complementary colors to corresponding colors will themselves be a set of corresponding colors. A method of predicting corresponding colors is derived theoretically and shown to agree with data. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 371–381, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.