Effect of text‐background lightness combination on visual comfort for reading on a tablet display under different surrounds

Many tablets are designed to change display brightness or color with surround for enhancing visual comfort. Although both color and brightness of a surround may vary a lot, few studies investigated how text‐background lightness combination of a tablet display and surround jointly affect visual comfort, and how display white point affects visual comfort. In this study, 20 observers evaluated visual comfort of 20 text‐background lightness combinations of a 9.7‐inch tablet display through paired comparisons under five surrounds—a dark surround and four ambient lighting conditions comprising two levels of correlated color temperature (CCT)—3500 and 6500 K—and illuminance—300 and 3000 lx. The combination of a black text and a light‐gray background (i.e., L^*_background = 75.33; L^*_text = 1.6) was evaluated the most comfortable when there was ambient light regardless of CCT and illuminance. It was also evaluated the third most comfortable under the dark surround. The observers also evaluated the visual comfort of a dark text on five different white backgrounds under 3500 and 6500 K at 1000 lx. The color of the background that was judged as the most comfortable neither had the whitest appearance nor matched the color of the ambient light. The simultaneous adjustment of the display white point and the text‐background lightness combination merits further investigations.     

RIT‐DuPont supra‐threshold color‐tolerance individual color‐difference pair dataset

The RIT‐DuPont dataset has been used extensively for formula development and testing since its inception during the 1980's, for example, in the development of CIE94 and CIEDE2000. The dataset was published as 156 color‐tolerances, T₅₀, along specific vector directions about 19 color centers. Probit analysis was used to transform judgments of 958 color‐difference pairs by 50 observers to these 156 tolerances. For most statistical significance testing, the number of samples determines the confidence limits. Thus, there was an interest in publishing the individual color‐difference pair visual and colorimetric data to improve the precision of significance testing. From these 958 pairs, 828 pairs had determinable visual differences. The others had either excessive visual uncertainty or had unanimous visual judgments such that visual differences were undefined. In addition, a method was devised to assign visual uncertainty to each of these pairs using the principles of maximum likelihood and the T₅₀ values. Comparisons were made between the T₅₀ and individual color‐difference pair data both including and omitting uncertainty weightings. The weighted dataset was found to be equivalent to the T₅₀ tolerances. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2010     

Determination of oil color by image analysis

An image analysis method was developed to determine oil color based on Lovibond-red according to Chinese standard GB5528-85. For one hue, one feature of image analysis, a negative correlation with the red reading of the Lovibond tintometer (r>0.995) was found. Mathematical models were designed to measure oil color based on image analysis. Fortynine oil samples including peanut oil, soybean oil, fragrant sesame oil, rapeseed oil and cottonseed oil were evaluated to assess the performance of the image analysis models. Good agreement, r ²=0.999, between image analysis values and visual color measurement by using the Lovibond Color Scale was obtained.     

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     

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     

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     

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 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.     

Analysis of five sets of color difference data

In a systematic optimization process five sets of recent color difference data have been analyzed for commonalities. Adjustment of the X tristimulus values and application of a systematic, surround dependent S_L function was found to be beneficial in all cases. Other modifications of the CIE94 color‐difference formula were found to bring improvements only in some cases and may be spurious. Application of what seem to be nonsystematic scale factors in a range of 0.78–1.38 improve correlation between calculated and visual color differences in all cases. After optimization, calculated color difference values explain between 80–90% of the variation in visual color differences. Some of the datasets are shown not to be well suited for formula optimization. Optimization in all cases by set, for three sets of data by quadrant in the a^*b^* diagram, and for one set by subset did not reveal any additional systematic trends for improvement. It appears that the basic structure of CIE94, with the recommended modifications, is a good approximation as a model for color‐difference evaluation in the range from 0.5–10 units of difference. The model is surround dependent. A number of issues remain to be resolved. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 141–150, 2001     

Effects of cold plasma on the color parameters of Hyssop (Hyssopus officinalis L.) using color imaging instrumentation and spectrophotometer

This research was conducted to evaluate the effects of cold atmospheric plasma treatment on the color of Hyssop (Hyssopus officinalis L.) and also to compare the usage of the spectrophotometer vs the color imaging instrumentation for the evaluation of the treatment on the color parameters. The experiments were investigated at different treatment times of 1, 5, and 10 minutes and the voltage values of 17, 20, and 23 kV. Possible changes of color were evaluated by using CIE L*a*b* values obtained with HunterLab colorimeter and CIE L*a*b* values obtained with a digital still camera (DSC) using digital image processing (MATLAB software). The values of L*, a*, and b* of the samples were obtained using both the methods. The results revealed that the L*, a*, and b* values of the treated Hyssop samples changed with increasing the treatment time and the voltage applied. Evaluating the interaction effects revealed that there was a significant difference in the (−a^*/b^* ) ratio. In addition, the results showed that the effects of all variables on the color parameters were significantly different in the case of the DSC using digital image processing. However, these effects were not significantly different using HunterLab colorimeter except for time variable and interaction effects of a* and (−a^*/b^* ) ratio. The lightest green color and the maximum chlorophyll content loss were observed for 23 kV applied over 10 minutes. Based on the results, the digital image processing can be used as a practical tool to study the variations at the color of dried Hyssop leaves after cold plasma treatment.     

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     

What can we learn from a dress with ambiguous colors?

We performed objective spectroradiometric measurements on an LCD image of the recently famous Tumblr dress which is typically perceived by people as blue/black or white/gold. The average ± standard deviation of the CIELAB coordinates was as follows: For a set of 33 points in the areas considered as blue/white, L* = 46 ± 6, C*_ab = 33 ± 6, and h_ab = 282 ± 3°, and for a set of 36 points in the areas considered as black/gold, L* = 29 ± 6; C*_ab = 10 ± 4; h_ab = 16 ± 34°. Initially, this first set of values has low variability and corresponds to a blue color, whereas the second set of values has a very large hue‐angle range, including points which can be considered as both gold and black colors. We also performed spectrophotometric measurements on an original model of this dress, and, assuming D65 illuminant and CIE 1931 colorimetric standard observer, the average results were L* = 26, C*_ab = 39, and h_ab = 289°, and L* = 10, C*_ab = 1, and h_ab = 290° for the blue/white and black/gold points, respectively. We discuss the influence of different factors on the blue/black and white/gold perceptions of different people, including observers' variability in color‐matching functions, Bezold–Brücke and Abney effects, background influence, and illumination assumptions. Although more research on the effect shown in this dress is needed, we think that from this example we can learn that objects do not have specific colors; that is, color is a human perception, and many times the answer of the human visual system is not simple and relies on assumptions of unknown, and variable, origin. © 2015 The Authors Color Research & Application Published by Wiley Periodicals, Inc., 40, 525–529, 2015     

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.     

Development of a comprehensive visual dataset based on a CIE blue color center: Assessment of color difference formulae using various statistical methods

The objectives of this work were to develop a comprehensive visual dataset around one CIE blue color center, NCSU‐B1, and to use the new dataset to test the performance of the major color difference formulae in this region of color space based on various statistical methods. The dataset comprised of 66 dyed polyester fabrics with small color differences ($\Delta E_{{\rm ab}}^* < 5$ ) around a CIE blue color center. The visual difference between each sample and the color center was assessed by 26 observers in three separate sittings using a modified AATCC gray scale and a total of 5148 assessments were obtained. The performance of CIELAB, CIE94, CMC(l:c), BFD(l:c), and CIEDE2000 (K_L:K_C:K_H) color difference formulae based on the blue dataset was evaluated at various K_L (or l) values using PF/3, conventional correlation coefficient (r), Spearman rank correlation coefficient (ρ) and the STRESS function. The optimum range for K_L (or l) was found to be 1–1.3 based on PF/3, 1.4–1.7 based on r, and 1–1.4 based on STRESS, and in these ranges the performances of CIEDE2000, CMC, BFD and CIE94 were not statistically different at the 95% confidence level. At K_L (or l) = 1, the performance of CIEDE2000 was statistically improved compared to CMC, CIE94 and CIELAB. Also, for NCSU‐B1, the difference in the performance of CMC (2:1) from the performance of CMC (1:1) was statistically insignificant at 95% confidence. The same result was obtained when the performance of all the weighted color difference formulae were compared for K_L (or l) 1 versus 2. © 2009 Wiley Periodicals, Inc. Col Res Appl, 2011     

Effect of Silicone Nano,Nano/Micro and Nano/Macro‐Emulsion Softeners on Color Yield and Physical Characteristics of Dyed Cotton Fabric

The study of silicone nano‐emulsions and softeners to alter physical properties of undyed cotton fabric has recently gained a substantial interest. However, systematic investigation of silicon nano‐emulsion softeners on dyed cotton fabric has not so far been conducted. This paper deals with the application of silicone nano‐, micro‐, and macro‐emulsion softeners, and combinations of nano/micro and nano/macro, on dyed cotton fabric. We report the effect of silicon nano/micro‐ and nano/macro‐emulsion softeners on color yield and physical characteristics of dyed cotton fabric. All bleached fabrics were dyed with CI Reactive Black 5 and then treated with known concentrations of silicone softeners by the pad‐dry method. The silicone nano‐emulsion was combined with micro‐ and macro‐emulsion softeners using blending ratios of nano/micro (1:1) and nano/macro (1:1). Treated fabrics were compared in terms of physical properties such as fabric handling, wrinkle recovery angle, bending length, abrasion resistance and tensile strength. The color changes were evaluated by color yield (K/S) values and total color difference (ΔE_cmc). The results revealed that the silicon nano‐emulsion had better physical properties than micro‐, macro‐ and combination nano/micro‐ and nano/macro‐emulsion softeners. Among all treated samples, nano‐emulsion softeners showed better ΔE_cmc values. Scanning electron microscopy analysis suggests that the fiber morphology of treated fabrics was very smooth and uniform.     

Multi-angle measurement device for analysis of coating appearances

Material appearance is an important factor in product design because it tends to attract consumers. Effect coatings that contain aluminum and interference pigments are widely used in automobiles. The color and sparkle values of these coatings vary with the observation angle. Several methods have been proposed to study these variations with respect to the observation angle. However, a high angular resolution is required to understand human perception. Thus, we developed a measurement device that can analyze the color and sparkle values at a high angular resolution for multiple angles. The device captures the sample surface by using a line scan spectral camera. Because the lighting device rotates around the sample while the line scan camera is capturing images, each scan line that makes up the image contains different angle information. A comparison was performed among the results obtained from this study, a commercially available device, and a subjective value of sparkle perception. Twenty samples were required to confirm that the sparkle values calculated from this study correlated closely with the subjective scores. In addition, the difference between the color values obtained from the BYK-mac i spectrophotometer and the proposed technique was less than 3.5 (ΔE₀₀). The findings of this study will help in developing the appearance of effective coatings. In addition, using evaluation values, quality control, and improvement are possible after mass production of coatings.     

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     

Evaluation of the quality of different D65 simulators for visual assessment

In this study psychophysical experiments were conducted to investigate the visual color differences of 77 textile metamers using a gray‐scale rating method under five D65 simulators. The quality of each of the D65 simulators was quantified according to the method provided in CIE Publication No. 51.2 using the visible range metamerism index (MI_vis). The five D65 simulators were categorized from A to D according to their MI_vis values. The color difference of each metameric pair was calculated using the spectral power distribution (SPD) of CIE illuminant D65 and artificial SPDs of D65 simulators. The performance factor (PF/3) was used to indicate the agreement between visual differences under five D65 simulators as well as between instrumental color difference and visual difference. Observer accuracy and observer repeatability were also analyzed by PF/3 measure. The experiment results showed that the visual data obtained from category A and B D65 simulators were in good agreement with the PF/3 measure and had no statistical difference in a pair comparison t test. The results also indicated that better agreement between instrumental and visual color differences was obtained using the artificial SPDs of the D65 simulator than with the SPD of CIE illuminant D65. The general color rendering index, Ra, for each D65 simulator was calculated by the CIE No. 13.3 method. © 2002 Wiley Periodicals, Inc. Col Res Appl, 27, 243–251, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10061     

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.