A new color image space HRU related to the CIEL*a*b* color space

The color image space with one kind of merit color image scales (WIP) is derived using the psychophysical method of magnitude estimation method, usually used in visual assessment of color appearance, which can be used to describe the color image meanings of colors of works of art in parallel with those of people. The results show that a new color image space HRU is developed, and the relativity between this space and the CIEL*a*b* color space is also discussed. And, a good relationship between the HRU color image space and the CIEL*a*b* color space can be found. This may be of great advantage to the new color image space HRU in predicting the color image for single color based on the color space CIEL*a*b*. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 452–457, 2009     

Color classification of natural color images

The present article describes a color classification method that partitions a color image into a set of uniform color regions. The input image data are first mapped from device coordinates into the CIE L*a*b* color space, an approximately uniform perceptual color space. Colors used to represent a natural color image are classified by means of cluster detection in the uniform color space. The basic process of color classification is based on histogram analysis to detect color clusters sequentially. The principal components of a color distribution are extracted for effective discrimination of clusters. We present an algorithm for sequential detection of color clusters in the uniform color space, and the related algorithms for region processing and color computation. The performance of the method is discussed in an experiment using three kinds of natural color images.     

Applying image‐based color palette for achieving high image quality of displays

In the highly competitive display market, manufacturers continuously develop new technologies to improve the image quality of displays. However, color measurement and visual assessment are time‐consuming to production lines. A new method to measure and improve color quality of the displays automatically therefore, is urgently needed to the manufacturers. This article proposes a familiar color correction strategy to optimize the colors of different displays by means of creating an image‐based color palette which enables color correction for familiar objects (e.g., facial skin, blue sky, or green grass) in the multidisplay systems. To produce the image‐based color palette, the 8‐bit RGB value of each pixel in an image is transformed to L*d*n* (lightness/dominant color/nondominant color) color channels, and the dominant‐color regions in an image are subsequently extracted from the dominant color (d*) channel. The memory color data of familiar objects can be set in reference monitor in advance to determine the dominant color (d*) channel. Then a series of palette colors are generated around a displayed image. The color palette will be displayed as a target for two‐dimensional colorimeter shooting to obtain the measured color data. The familiar color correction model was established based on a first‐order polynomial regression to achieve a polynomial fit between the measured color data and the reference color data on the color palette. The proposed method provides a solution to correct familiar colors on a displayed image, and maintains the original color gamut and tone characteristic in the multidisplay systems simultaneously. It is possible to achieve the preferred intent of the displayed images by using the proposed familiar color correction method. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 154–168, 2014     

A general form of color difference formula based on color discrimination ellipsoid parameters

A general color difference formula has been derived based on the parameters of color discrimination ellipsoids in the CIE 1976 L*a*b* color space. By using different orders of approximation, the general formula resembles the basic forms of the current formulae. The method described in this article suggests a framework for modifying the CIE 1976 L*a*b* color difference formula.     

Quantitative evaluation of color harmony via linguistic-based image scale for interior design

In order to develop a computer-based color consultation system, a quantitative evaluation of color harmony for interior images is constructed by using a linguistic-based image scale. Conventionally, color harmony from any engineering perspective has received limited attention, because its successful evaluation requires the development of pleasure-related features. In this study, a new pleasure-related function of color linguistic distribution (CLD) is proposed to quantitatively represent the mental color impression of interior images upon a designed one-dimensional linguistic-based image scale of EXCITING-CALM. Supported by a database that includes fashion trends, the distribution state of CLD is capable of indicating the fashion trends in Taiwan. Also, on the image scale, the grade of color harmony can be measured by its CLD similarity to a reference harmony distribution (RHD). Evaluated results demonstrate that interior design is primarily dominated by (low) saturation and (high) brightness. Also, results based on CIE1976 L*a*b* and CIE1976 L*u*v* color spaces are observed to be better than those based on the hue-dominated method of Moon-Spencer theory in corresponding with human's linguistic similarity, questionnaire-based harmony evaluation, and social trends. Moreover, our solution percentage has been investigated as 72.3%, which is better than those of the Moon-Spencer theory and of the “Law of Inverse Ratios of Areas.” For application purposes, the proposed system is suitable to analyze the mental impression of products from the perspective of the customer. © 1996 John Wiley & Sons, Inc.     

Using principal component analysis technique in the instrumental shade sorting of textile fabrics

In the present study, the CCC shade sorting method was employed with CMC(2:1) color difference formula on the colorimetric data (CIEL*a* b*) of 37 fabric color sets. The k‐means non‐hierarchical clustering technique was also combined with the CCC shade sorting method to increase its efficiency. The results of this combined method showed a slightly better performance, as compared with the CCC method. Also, a new proposed shade sorting method by the application of principal components analysis (PCA) technique was used to identify and remove the outliers in each of the color sets. The results of separating the outliers showed that although the diameter of group criterion was improved significantly, the number of groups, the number of singleton groups, and the number of groups with low samples were increased considerably. Finally, in a second new proposed shade sorting method, PCA was used as a data reduction tool on the colorimetric data of the 37 color sets. Then, the two first principal components in combination with a k‐means clustering technique were used for the clustering of the samples in each color set. The results of this second new proposed method were found to be similar to the CCC method considering number of group and fabric consumption criteria. The second new proposed method revealed a moderately worse result, with regard to the diameter of group criterion, than the CCC method.     

Iris color and texture: A comparative analysis of real irises,ocular prostheses,and colored contact lenses

In this work, we analyzed the color and texture of irises, ocular prostheses, and cosmetic colored contact lenses measured by means of a multispectral system, which provides the CIE L*a*b* colorimetric coordinates of a high resolution image pixel by pixel. The same subject, who has dark brown irises, participated in the measurement of all the contact lenses. The CIE L*a*b* colorimetric coordinates were analyzed to classify the samples into three major groups (brown, blue and green) using a new algorithm developed for this purpose. This classification allowed us to carry out a comparison of the color associated with each set of samples, using the corresponding color gamuts in the CIE L*a*b* color space. Furthermore, we analyzed the iris color reproduction achieved by prostheses and contact lenses in terms of CIEDE2000 color differences, and obtained closer results with prostheses. In addition, we performed an analysis of texture by means of the color spatial distribution of all samples. This was achieved by means of two statistical approaches: first order statistics of image histograms and second order statistics using co‐occurrence matrices. The results suggest that the texture associated with real irises, ocular prostheses and colored contact lenses is very different. This study provides useful information about the color and texture of irises that may help to establish a strategy for improving the techniques used in the manufacturing process of prostheses and colored contact lenses to obtain a better and more realistic appearance. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2011     

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     

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017     

A rapid estimation of pigment content in virgin olive oils by a solvent-free method

In this work, a fast method was proposed for estimating the virgin olive oils (VOOs) carotenoids and chlorophylls concentration using color measurement. The pigment content by conventional spectrophotometry method and CIELAB color (L*, a*, and b*) at different degree of sample thickness (from 5 to 50 mm) of one hundred VOOs were measured. Oil carotenoids and chlorophylls content were correlated with the color parameters for the different oil thickness studied to design the prediction models of the new method. The best regression coefficients (R²) were obtained for multiple linear regression model using the three independent variables (L*, a*, and b*) together measured at 5 mm of oil thickness. The R² were 0.9679, 0.9515, and 0.9644 for predicting carotenoids, chlorophylls, and total pigments, respectively. External validation of these prediction models was satisfactory (relative error < 0.1). Therefore, this new solvent-free colorimetric method is a useful method for determination of carotenoids and chlorophylls content in VOOs. Practical applications: The simple colorimetric method developed in this study offers a fast and accurate alternative to current methods published in the literature to estimate the pigment content in VOOs. It is a rapid (less than 1 min) and cheap method, with the advantage of ease of operation, no sample pretreatment and solvent-free, thus environmentally friendly. This methodology can potentially be used by trained “nonprofessional analytical skilled” people in small laboratories or olive oil mills with limited technical facilities. Therefore, the technique is highly plausible as an alternative to determine the pigment content in VOOs. Finally, future works with this methodology could be carried out to online control of VOOs pigments content in the oil extraction process.     

Subjective evaluation of uniform color spaces used for color-rendering specification

Three attributes of color appearance of sixteen samples illuminated by various light sources were estimated directly by two observers. The results are plotted on a subjective chroma diagram, with which the psychometricchroma diagrams calculated from the CIELUV and CIELAB spaces are compared. The comparison and numerical evaluations show that the CIELAB space is much better than the CIELUV and U* V* W* spaces in simulating surface-color appearance under various illuminants. Substitution of the CIELAB space for the U* V* W* space used in the CIE method of color-rendering specification is proposed.     

A novel analysis of color component for top dyed melange yarn with support vector machine

In view of the difficulty of distinguishing the color component in top dyed melange yarn due to the spectral overlap of the component colors, a novel color component analysis method based on support vector machine is presented. With this method, spectra data can be distinguished more accurately and effectively than with the traditional method—human‐eye detection—and therefore, the method will be very helpful for accurate color matching. In our work, the core idea was to convert the overlapped spectra data into linearly separable ones in a high dimension space, followed by recognition and determination of the composition of melange yarn by trained support vector machine classifier. The effects of four kernel functions, i.e., linear, radial basis kernel, sigmoid, and polynomial, as well as five spectral preprocessing methods, including amplification, first derivative, second derivative, principal components analysis, and L*a*b* values were studied. The results demonstrated that with the amplification factor of 100 of reflectance spectra coupled with L*a*b* as input data, and using radial basis kernel as kernel function, the highest recognition rate was achieved, with an average recognition rate of eight colors of 96.5%, indicating that it was a better color component analysis method for top dyed melange yarn. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 636–641, 2016     

多功能分散剂对色母粒性能的影响

研究了分散剂YY–5023及其与聚乙烯(PE)蜡复配对色母粒中酞青蓝分散性的影响。结果表明,随着分散剂用量的增加,色母粒的熔体流动速率(MFR)增大,平衡扭矩降低,达到最大扭矩所需时间延长;当YY–5023用量为2%和4%时,平衡扭矩分别为5.3 N·m和4.4 N·m,比未添加分散剂时的平衡扭矩分别下降28.4%和40.5%,达到最大扭矩的时间分别为53.5 s和57.3 s;复合分散剂(YY-5023与PE蜡配比为1∶1)用量为2%和4%时,平衡扭矩分别为5.7 N·m和5.1 N·m,比未添加分散剂时的平衡扭矩分别下降23.0%和31.1%,达到最大扭矩的时间分别为48.5 s和52.3 s。当复合分散剂用量为2%时,色母粒中酞青蓝的分散效果最好,L*值、a*值、b*值分别为34.19,-9.82,-31.82,低密度聚乙烯薄膜表面的色点和晶点数目小于3个。     

Effect of texture on color variation in inkjet‐printed woven textiles

Inkjet‐printed textiles are influenced by a wide range of parameters due to highly diverse textile structures and the resulting textures. The goal of this study is to understand the effect of texture on color appearance in inkjet‐printed woven textiles. Cotton‐woven samples were constructed with nine different weave structures. Each sample was digitally printed with identical squares of primary colors cyan, magenta, and yellow and secondary colors red, green, and blue. The amount of ink applied was controlled consistently with an image editing software. CIE L* values were calculated from the measured reflectance. 25 observers ranked the perceived texture and color lightness of each sample. Perceived visual texture and perceived color lightness scales were estimated from the rankings using the rank order method. The measured CIE L* values and the scale of perceived lightness were positively related for the primary and secondary colors. Instrumental measurements of the textile surface characteristics were positively related to the visual scale. Texture was demonstrated to cause a measurable effect on color results in inkjet printing, both using instrumental and perceptual measures. To investigate if the color differences were substantial enough to cause “out of tolerance” ratings in textiles based on common textile industry color acceptance procedures, color differences among the samples were calculated and compared to a reference sample. Results demonstrated that color variation due to texture was sufficient to lead to rejection of a printed color in comparison to a color specification. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 297–303, 2015     

Application of Ozone in Stripping of Cotton Fabric Dyed with Reactive Dyes

ABSTRACT

This paper discusses the color stripping of cotton fabric dyed with four reactive dyes using ozone. An ozone color stripping process has been optimized in terms of pH, ozone dose, treatment time and dye concentration. Color stripping efficiency of the process is evaluated in light of the color properties including Lightness (L*), Lightness Difference (?L*), and Color Difference (?E*) in addition to strength properties, i.e., copper number and degree of polymerization as well as compared with a control method (conventional reductive color stripping method). The results demonstrate maximum color stripping at an ozone dose of 10 g/h, exposure time of 45 min, and pH maintained at 5. The Stripping Efficiency, however, decreases with increasing dye concentration from 0.5% to 4%, which implies that decolorization of dyed fabric with higher initial dye concentration requires higher ozone dose and/or longer exposure time. The strength of the ozone-stripped fabric sample is almost similar to that of control. The chemical oxygen demand of effluent generated by ozone stripping process is 97% lower than the effluent produced by the conventional stripping method. The Ozone Stripping Process hence proves to be environment-friendly as it consumes less water, energy, and chemicals.     

Comparison of blue pigments prepared by two different methods

The color efficiency of ceramic glaze blue pigments obtained by synthesis methods was compared. the fired pigments and enameled samples were characterized by XRD, UV-vis-NIR spectroscopy, CIE-L^*a^*b^* color-measurements, and SEM. The pigments obtained by the Pechini method presented a better solubility in the molten glazes than the pigments obtained by the mechanical mixture of the oxide precursors. The pigments obtained by the Pechini method also developed a bluer color hue than the pigments obtained by the mechanical mixture of the oxide method.     

Testing the differences between two color measurement probability distributions using Hotelling's T2 test and the permutation test

It is common practice in statistics to test the equality of two population means using, for example, the Student's t test, in the univariate case, or the Hotelling's T² Test in the multivariate case. However, tests on the equality of population means are not well developed for testing the difference between two populations of color measurements. Methods for analyzing populations of spectral reflectance and L*a*b* measurements have been described for applications such as analyzing inter-instrument agreement and repeatability. Methods have also been proposed for the analysis of color differences, but there are little written about techniques for testing whether two samples have the same probability distribution. This article focuses on testing the difference between color measurement probability distributions based on color difference. In addition, a metric is proposed called the threshold for color difference discrimination (TCDD, in units of ΔE), the color difference at which two populations can be considered to have different population distributions. A lower TCDD means smaller color differences between two samples can be resolved. Two parametric tests based on Hotelling's T² test and a nonparametric permutation test were used to determine the TCDD for populations of color measurements with different variances and sample sizes. The TCDD was found to be smaller by tests using the Hotelling's T² statistic, compared with a permutation test performed directly on color difference. It was also found, as expected, that larger sample sizes led to smaller TCDDs, as did smaller population variances.     

Synthesis and characterization of Y (In,Mn) O3 blue pigment using the complex polymerization method (CPM)

In this paper, a new synthetic pathway is proposed for the system YIn_1-xMn_xO₃, a bright blue inorganic pigment, discovered in 2009. Blue pigment samples with increasing concentration of Mn³⁺ (x?=?0.08, 0.12 and 0.16) were prepared using the complex polymerization method (CPM) and compared with those synthesized via solid state reaction. All powders, the amorphous precursor from CPM and the starting materials for solid state method, were calcined at 1000, 1100, 1200 and 1300?°C for 12?h, and the resulting blue pigments were characterized by X-ray diffraction (XRD), colorimetric system CIE L*a*b* and Near infrared (NIR) reflectance measurements. XRD patterns and Rietveld Refinement show that the lowest temperature at which single hexagonal phase (isostructural to YInO₃) is formed is 1000?°C for CPM method and 1300?°C for conventional solid state method, respectively. The L*a*b* values demonstrate that the coloration of powders prepared by CPM exhibit temperature dependence below 1300?°C, a color shade shift from grayish blue to intense deep blue is observed when heating the samples from 1000 to 1300?°C. Blue pigments obtained by CPM have smaller particle size due to low temperatures and excellent near-infrared reflectance comparable to those by solid state method. Thus, providing advantages for application process and energy efficiency.