Optical implementation of spectral filtering for the enhancement of skin color discrimination

Light reflected from an object contains a range of information about its physical and chemical properties. Changes in the physical properties of an object can sometimes be evident as barely detectable changes of color. Our earlier study (Proceedings of the 15th Color Imaging Conference, Albuquerque, New Mexico, 2007. p 195–200) proposed a method for designing a spectral filter to enhance visual discrimination. Two filters were designed: one to discriminate skin and vein colors on human arms, and other to discriminate human facial colors in the presence and absence of cosmetics. In this study, the filters with theoretically designed spectral transmittances were implemented as real optical filters. Visual inspection of RGB color images taken with a digital camera through the developed optical filters showed clear enhancement of discrimination of two preselected colors. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2012     

Color errors of digital cameras

The mean color errors of a high‐quality digital camera are defined in CIELAB and CIEDE2000 ΔE units by using 16 ceramic color samples, whose accurate CIELAB values have been measured by a calibrated spectrophotometer. The bandwidths of CCD's color filters are evaluated by taking photographs of CRT‐display primaries. The lowest mean color errors were 13.1 CIELAB ΔE units and 8.1 CIEDE2000 ΔE units before corrections. Large color errors are decreased successfully by using three different methods: simple photoeditor, gamma correction, and multiple regression. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 217–221, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20007     

Physics‐based spectral sharpening through filter‐chart calibration

The spectral overlap of color‐sampling filters increases errors when using a diagonal matrix transform, for color correction and reduces color distinction. Spectral sharpening is a transformation of colors that was introduced to reduce color‐constancy errors when the colors are collected through spectrally overlapping filters. The earlier color‐constancy methods improved color precision when the illuminant color is changed, but they overlooked the color distinction. In this article, we introduce a new spectral sharpening technique that has a good compromise of color precision and distinction, based on real physical constraints. The spectral overlap is measured through observing a gray reference chart with a set of real and spectrally disjoint filters selected by the user. The new sharpening method enables to sharpen colors obtained by a sensor without knowing the camera response functions. Experiments with real images showed that the colors sharpened by the new method have good levels of color precision and distinction as well. The color‐constancy performance is compared with the data‐based sharpening method in terms of both precision and distinction. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 564–576, 2015     

Concerning the calculation of the color gamut in a digital camera

Several methods to determine the color gamut of any digital camera are shown. Since an input device is additive, its color triangle was obtained from their spectral sensitivities, and it was compared with the theoretical sensors of Ives‐Abney‐Yule and MacAdam. On the other hand, the RGB digital data of the optimal or MacAdam colors were simulated to transform them into XYZ data according to the colorimetric profile of the digital camera. From this, the MacAdam limits associated to the digital camera are compared with the corresponding ones of the CIE‐1931 XYZ standard observer, resulting that our color device has much smaller MacAdam loci than those of the colorimetric standard observer. Taking this into account, we have estimated the reduction of discernible colors by the digital camera applying a chromatic discrimination model and a packing algorithm to obtain color discrimination ellipses. Calculating the relative decrement of distinguishable colors by the digital camera in comparison with the colorimetric standard observer at different luminance factors of the optimal colors, we have found that the camera distinguishes considerably fewer very dark than very light ones, but relatively much more colors with middle lightness (Y between 40 and 70, or L* between 69.5 and 87.0). This behavior is due to the short dynamic range of the digital camera response. © 2006 Wiley Periodicals, Inc. Col Res Appl, 31, 399–410, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20245     

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     

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     

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     

A new manufacturable filter design approach for spectral reflectance estimation

Selection of the best possible filter set among a set of available filters is the obvious method of increasing dimension of camera signals for spectral reflectance reconstruction. There are also methods that are focusing on the filter design regardless of noticing to the constructability of the designed filters. This study shows that direct optimization of physical variables of filter manufacturing technique is more reliable than indirect approach of designing and then physical manufacturing of the designed filters. Direct optimization of the transmission‐controlling primaries in filter manufacturing process would guarantee having the designed filters in reality. Combination of some solvent dyes was used as the spectral transmission matching system for filter manufacturing. As a conventional technique, filters were designed and best possible dye concentrations that match the desired filters were calculated. As an alternative approach, filters were also designed using direct optimization of dyes concentrations. The results showed that direct optimization of dye concentrations exhibits better performance in comparison with the conventional technique. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 316–326, 2017     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.     

Research on filter selection method for broadband spectral imaging system based on ancient murals

Digitization of cultural heritage protection has received considerable attention in heritage studies and spectral imaging technology has been playing an important role in this research. This article aims to study the technique of selecting optimal filter set to obtain ancient murals spectral image with high spectral and colorimetric accuracy based on the broadband spectral imaging system. The 330 Dunhuang murals mineral pigment color patches and the GretagMacbeth ColorChecker (CC) as well as 27 pieces of optical filters chosen as samples were examined. For each piece of filter, the three‐channel image was captured by the spectral imaging system. Then, 351 groups of six‐channel digital count images were acquired by arbitrary combinations of two among the 27 three‐channel digital count images. The pseudo‐inverse, principal component analysis, and R‐matrix methods were used to reconstruct the spectral reflectance from the six‐channel digital counts for each sample. Finally, this study identified the optimal filter set by evaluating the integrated error (TOTAL ERROR), which was calculated by normalizing the mean spectral root‐mean‐square error (RMS), mean spectral goodness‐of‐fit error (1‐GFC), and mean CIEDE2000 color difference (ΔE₀₀) and by multiplying them together. After the optimal optic filter set was selected, it was applied to the Dunhuang murals spectral imaging and was evaluated. The results showed that the optimal optic filter set could result in promising improvement both in spectral and color accuracy when compared with the production camera. In addition, it can be used for the construction of Dunhuang murals spectral image database. © 2015 Wiley Periodicals, Inc. Col Res Appl, 41, 585–595, 2016     

Color and visual complexity in abstract images: Part II

There are a limited number of studies examining color, visual complexity, and visual interest together, and one of the recent studies that tried to bring a new understanding to the association between color, visual complexity, and visual interest was the first part of the current study. Most of the well‐known color studies researching the effects of color on psychology, physiology, emotion, mood, attention, well‐being, visual complexity, and visual interest used isolated color patches that might be lacking in reflecting the dominating factors. Thus, the aim of this study was to find the relationship between visual complexity, visual interest, and color difference (ΔE) values of colors in abstract images, and it was hypothesized that, as the average ΔE value of colors in an abstract image increases, visual interest and visual complexity will increase until reaching a threshold where visual interest and visual complexity start to decrease. In order to test the hypothesis, a new abstract image was generated and colored. The generated abstract image was rated by 120 undergraduate students from the Faculty of Art, Design and Architecture. As the results of the study indicated, there was an inverted U‐curve relationship between average ΔE values and both visual interest and visual complexity in abstract images.     

Formulating Pantone colors by unused base inks,formulation software,and a spectrophotometer

Unused base inks that are not going to be used for printing production are considered to be hazardous materials. Their disposal is expensive, and strict environmental regulations should be followed for their disposal. As an alternative, this article describes how spectral data of unused base inks can be gathered and mixed to generate new colors to incorporate them back to print production for small‐volume jobs. In this study, 30 different Pantone colors were selected as target colors. The CIE L*a*b* spectral data of Pantone colors and unused base inks were gathered via a spectrophotometer. A commercial formulation software, based on multiflux theory and CIE L*a*b* color space, was used to formulate ink recipes that contained the base inks. To quantify the performance of ink recipes, they were mixed and printed using an offset printability tester. The CIELAB ΔE*_ab metric, developed by CIE, was used to detect the visual differences between the target Pantone Color and printed colors.     

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     

Irradiance‐independent camera color calibration

For a digital color camera to represent the colors in the environment accurately, it is necessary to calibrate the camera RGB outputs in terms of a colorimetric space such as the CIEXYZ or sRGB. Assuming that the camera response is a linear function of scene luminance, the main step in the calibration is to determine a transformation matrix M mapping data from linear camera RGB to XYZ. Determining M is usually done by photographing a calibrated target, often a color checker, and then performing a least‐squares regression on the difference between the camera's RGB digital counts from each color checker patch and their corresponding true XYZ values. To measure accurately the XYZ coordinates for each patch, either a completely uniform lighting field is required, which can be hard to accomplish, or a measurement of the illuminant irradiance at each patch is needed. In this article, two computational methods are presented for camera color calibration that require only that the relative spectral power distribution of the illumination be constant across the color checker, while its irradiance may vary, and yet resolve for a color correction matrix that remains unaffected by any irradiance variation that may be present. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 540–548, 2014     

Evaluating the 1931 CIE color‐matching functions

The use of colorimetry within industry has grown extensively in the last few decades. Central to many of today's instruments is the CIE system, established in 1931. Many have questioned the validity of the assumptions made by Wright¹ and Guild,² some suggesting that the 1931 color‐matching functions are not the best representation of the human visual system's cone responses. A computational analysis was performed using metameric data to evaluate the CIE 1931 color‐matching functions as compared to with other responsivity functions. The underlying assumption was that an optimal set of responsivity functions would yield minimal color‐difference error between pairs of visually matched metamers. The difference of average color differences found in the six chosen sets of responsivity functions was small. The CIE 1931 2° color‐matching functions on average yielded the largest color difference, 4.56 ΔE. The best performance came from the CIE 1964 10° color‐matching functions, which yielded an average color difference of 4.02 ΔE. An optimization was then performed to derive a new set of color‐matching functions that were visually matched using metameric pairs of spectral data. If all pairs were to be optimized to globally minimize the average color difference, it is expected that this would produce an optimal set of responsivity functions. The optimum solution was to use a weighted combination of each set of responsivity functions. The optimized set, called the Shaw and Fairchild responsivity functions, was able to reduce the average color difference to 3.92 ΔE. In the final part of this study a computer‐based simulation of the color differences between the sets of responsivity functions was built. This simulation allowed a user to load a spectral radiance or a spectral reflectance data file and display the tristimulus match predicted by each of the seven sets of responsivity functions. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 316–329, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10077     

Influence of a holistic color interval on color harmony

This study investigates how a holistic color interval, i.e., the nondirectional color difference between a pair of colors in a CIELAB uniform color space, influences perceived color harmony. A set of 1035 test color pairs displayed on a CRT was evaluated for the degree of harmony. These test color pairs consist of pairs combined from among the selected 46 test colors evenly distributed in color space. The subjects were asked to select their three preferred colors from these 46 test colors and then to evaluate the degree of harmony of the test color combinations. The color intervals (ΔE) of each test color combination were calculated and treated as values of an independent variable. In addition, the evaluated degrees of color harmony were considered as values of a dependent variable, in which statistical analysis confirmed the relationship: the degree of harmony is a cubic function of the color interval. Moreover, the plot of this relationship allowed us to identify four color intervals: roughly corresponding to the regions of first ambiguity, similarity, second ambiguity, and contrast in Moon and Spencer's model. However, our results indicated that Moon and Spencer's principles for classifying harmonious/disharmonious regions in terms of the color interval for three color attributes—lightness, chroma and hue—may be inappropriate in predicting perceived color harmony. As for the color intervals between a pair of colors considered as a function of the three attributes, the interval for lightness may have a predominant effect on color harmony, expressed in terms of a cubic relationship. Results of the study further demonstrated that the subject's choice of colors significantly influences perceived color harmony. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 29–39, 2001     

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     

Color samples for colorimetric fidelity testing in television

The case for testing television (TV) colorimetric reproduction performance for groups of colors is discussed. The groups consist of colors which have some characteristic in common, like strong or moderate saturation. Such groups actually contain a large number of discernible colors but these are for testing purposes represented by a set with a limited number of reference samples. In recommended methods the evaluation of the performance for the group is based on the average of the individual color differences for the samples in the set. This average is here based on the CIE 1976 color difference ΔE_uv* and designated ΔE_a*. The question investigated is what properties the reference samples in the set should have to make D̊E_a* the best estimator of performance for the whole group. It is shown that the samples should be chosen for evenness in distribution and uniformity in lightness, preferably representative of average scene lightness. These points seem to have been overlooked for some presently recommended sets as illustrated by an example. Experimental data for TV cameras indicate that a collection of 10 color samples might be satisfactory for colorimetric acceptance testing.     

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