Surface color gamuts play a critical role in color management and in evaluating the color-reproduction capabilities of devices such as visual displays, printers, and other output media. Existing gamuts, most notably the Pointer Gamut and the ISO Reference Color Gamut (ISO 12640-3:2007), are defined by CIELAB coordinates under fixed viewing conditions (e.g., CIE illuminant C or D50). However, the reflectance spectra of their defining samples are not available, and these gamuts have been shown to inadequately represent the full diversity of real surface colors. Their applicability is further constrained by their dependence on specific illuminants. This study introduces a new surface color gamut derived from two comprehensive datasets: 142896 measured reflectance spectra from a wide range of materials, and 422 spectral power distributions (SPDs) of illuminants, including CIE standard illuminants (D65, A, D50), illuminant C, the F-series, daylight variants (D55, D75), and various LED sources. From these datasets, a representative subset of 501 reflectance spectra was selected to construct a gamut that remains valid across all 422 viewing conditions (SPDs combined with the CIE 1931 color-matching functions). The paper also presents visualization techniques for this new gamut and provides comparative analyses with the Pointer Gamut, the ISO-RCG, and standard color spaces such as sRGB, Display-P3, and BT.2020.

Globally, many species are threatened by population decline because of anthropogenic changes leading to population fragmentation, genetic isolation and inbreeding depression. Genetic rescue, the controlled introduction of genetic variation, is a method used to relieve such effects in small populations. However, without understanding how the characteristics of rescuers impact rescue attempts interventions run the risk of being sub-optimal, or even counterproductive. We use the red flour beetle (Tribolium castaneum) to test the impact of rescuer sex, and sexual selection background, on population productivity. We record the impact of genetic rescue on population productivity in 24 and 36 replicated populations for ten generations following intervention. We find little or no impact of rescuer sex on the efficacy of rescue but show that a background of elevated sexual selection makes individuals more effective rescuers. In both experiments, rescue effects diminish 6-10 generations after the rescue. Our results confirm that the efficacy of genetic rescue can be influenced by characteristics of the rescuers and that the level of sexual selection in the rescuing population is an important factor. We show that any increase in fitness associated with rescue may last for a limited number of generations, suggesting implications for conservation policy and practice.

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Two studies were conducted to investigate the impact of fresh pork display lamps on consumers’ sensory responses to pork products. In the first experiment, 63 participants were asked to evaluate pork products with different degrees of freshness under four fresh pork display lights and two LED lights. In the other experiment, 30 subjects participated in the Farnsworth–Munsell 100 hue test under the same lamps, with the aim of showing whether the fresh pork display lamps impaired color discrimination. The results showed that the light source had a significant effect on the color appearance evaluation of the pork products. The ratings for perceived freshness under the fresh pork display lamps were significantly higher than those of ordinary LED lamps, while the color discrimination performance of the subjects under those lamps was poor. It was demonstrated that improper component proportions of the light spectrum could influence observers’ assessment of meat quality and weaken the observers’ ability to distinguish the freshness level. Through this study, the authors would like to remind lamp users and manufacturers to not only be concerned about the improvement in the color appearance of pork, but also the need for consumers to be aware of the authentic freshness of the pork products.

Various facial colour cues were identified as valid predictors of facial attractiveness, yet the conventional univariate approach has simplified the complex nature of attractiveness judgement for real human faces. Predicting attractiveness from colour cues is difficult due to the high number of candidate variables and their inherent correlations. Using datasets from Chinese subjects, this study proposed a novel analytic framework for modelling attractiveness from various colour characteristics. One hundred images of real human faces were used in experiments and an extensive set of 65 colour features were extracted. Two separate attractiveness evaluation sets of data were collected through psychophysical experiments in the UK and China as training and testing datasets, respectively. Eight multivariate regression strategies were compared for their predictive accuracy and simplicity. The proposed methodology achieved a comprehensive assessment of diverse facial colour features and their role in attractiveness judgements of real faces; improved the predictive accuracy (the best-fit model achieved an out-of-sample accuracy of 0.66 on a 7-point scale) and significantly mitigated the issue of model overfitting; and effectively simplified the model and identified the most important colour features. It can serve as a useful and repeatable analytic tool for future research on facial impression modelling using high-dimensional datasets.

CIE has recently recommended a new color appearance model CIECAM16 to replace CIECAM02. It was also intended to recommend a uniform color space (UCS) based on CIECAM16, CIECAM16-UCS, for predicting color difference. However, there was a debate as to whether CIECAM16 can be used to predict color difference, since it takes tristimulus values calculated using the CIE 2 degrees observer as input, while tristimulus values for color difference are usually calculated using the CIE 10 degrees observer, as in the CIEDE2000 formula. Thus, further evidence is needed before CAM16-UCS can be recommended by the CIE as uniform color space. In this paper, we investigate the likely difference in color difference prediction when using CAM16-UCS, and its potential statistical significance. Firstly, the reflectance of each pair of color samples in the visual color difference datasets: BFD-P, Leeds, RIT-DuPont and Witt was generated based on the given tristimulus values. Then, color difference AE2,F and AE10,F, with F being the CAM16-UCS, can be computed under any illuminant using the 2 degrees and 10 degrees observers respectively. Comparison results showed that the difference between AE2,F and AE10,F was not statistically significant. Finally, both AE2,F and AE10,F were used to predict the visual color difference DV and the STRESS values between AE2,F and DV, and between AE10,F and DV respectively. Statistical tests showed that the differences between AE2,F and DV, and between AE10,F and DV were not significant. Hence this study shows that CAM16-UCS can be reliably used for predicting color difference. The findings are valuable for CIE to recommend CAM16-UCS as a uniform color space. Currently CIE TC1-98 is investigating the establishment of a new colorimetric system based on cone response tristimulus values LMS. It is expected that the reflectance datasets generated from the color difference datasets will be useful for the evaluation of any new colorimetric system.(c) 2023 Society for Imaging Science and Technology.

In this study, the third order polynomial regression (PR) and deep neural networks (DNN) were used to perform color characterization from CMYK to CIELAB color space, based on a dataset consisting of 2016 color samples which were produced using a Stratasys J750 3D color printer. Five output variables including CIE XYZ, the logarithm of CIE XYZ, CIELAB, spectra reflectance and the principal components of spectra were compared for the performance of printer color characterization. The 10-fold cross validation was used to evaluate the accuracy of the models developed using different approaches, and CIELAB color differences were calculated with D65 illuminant. In addition, the effect of different training data sizes on predictive accuracy was investigated. The results showed that the DNN method produced much smaller color differences than the PR method, but it is highly dependent on the amount of training data. In addition, the logarithm of CIE XYZ as the output provided higher accuracy than CIE XYZ.

Reliable and accurate skin colour measurements are of vital importance in a wide range of applications. This paper presents a comprehensive database of human skin colour and investigates the variations across ethnicities, genders, ages, and body locations. Nine separate data sets of spectrophotometric measurements of skin reflectance were collected from different laboratories around the world with 12 355 skin patches have been measured. Overall, skin colour distributions were found to largely overlap across ethnic groups. Variations in ethnicity were mostly found in the lightness and yellowness of skin, while differences between body locations were primarily found in the redness of skin. The lightness of skin was also consistently influenced by gender and females were found to have a lighter skin colour than males. Meanwhile, the younger subjects had slightly lighter skin than the elderly. The new database is expected to benefit numerous research fields and applications related to human skin.

The paper describes a comprehensive test to evaluate the performance of current colour-difference models using available experimental datasets. In total, 28 individual datasets were accumulated to test 17 colour-difference formulae, 13 of them based on Uniform Colour Spaces (UCSs) in terms of the Standardized Residual Sum of Squares (STRESS) measure. The 28 datasets were divided into three groups: Large Colour-Difference data (LCD), Small Colour-Difference data for surface colours (SCDs), and Small Colour Difference data for display colours (SCDd). For each colour model, four versions were tested: the original model, and that including k(L)-, Gamma- and k(L)/Gamma, which are the lightness parametric factor, the colour-difference exponent factor, and the combination of both, respectively, optimized to fit particular dataset(s). The statistical F-test was applied to test the difference between each pair of models. Furthermore, parametric effects between the large/small colour-difference magnitudes, and between surface/display colours were investigated. The results showed that CAM16-UCS significantly outperformed the other models for all groups. It accurately predicted all types of data and should be proposed for colour-difference evaluation across all industries.

With the rapid development of colour 3D printing technologies, colour-difference evaluation of 3D printed objects requires further studies to achieve faithful colour reproduction in 3D printing. The aim of this study is to collect visual colour-difference data of 3D printed objects in static and rotating conditions. The X-Rite virtual light booth with a rotating stage was used to conduct psychophysical experiments for 82 pairs of 3D printed samples with the grey-scale method. Five human observers with normal colour vision participated in the visual colour-difference assessments, and the collected experimental data were compared with the measurement data. The results showed that the perceptual colour differences of 3D samples in two conditions were similar, but the visual data of rotating samples have a poorer relationship (STRESS=39.38) with the measurement data, compared to the results of the static samples (STRESS=36.62). By using the optimised CIELAB colour-difference formulae in the previous study, the STRESS values decreased by 7 units.

Lipstick is one of the most commonly used cosmetics, which is closely associated with female attractiveness and influences people’s perception and behavior. This study aimed to investigate the impact of light sources, lipstick colors, as well as gender on the subjective assessment of lipstick color products from the prospective of color preference, purchase intention and sexual attractiveness. The correlation between color preference evaluations when applying lipstick on lips and on forearms was also explored. Sixty participants completed their visual assessment of 15 lipsticks worn by 3 models under 5 light sources, with uniformly sampled correlated color temperature (CCT) values ranging from 2,500 K to 6,500 K. The results indicated that the light source significantly influenced color preference and purchase intention, while lipstick color significantly impacted on sexual attractiveness. The interactions between gender and other factors were also observed and are discussed. Compared to men, women were found to be more sensitive to different light sources and hold different attitudes toward different lipstick colors under different CCTs. Interestingly, no significant correlation was found between lipstick color preference ratings on the lips and forearm, which conflicted with the commonly recognized way of lipstick color selection. These findings should contribute to a deeper understanding of the consumer attitude toward lipstick colors and provide a useful reference for lighting design in situations where cosmetics are specified, manufactured, retailed and generally used, both professionally and in the home.

Colour and texture characteristics convey most of the information of an image and influence human perception as contributing factors to perceived preference. How the colour, together with the texture characteristics affects fabric image preference is not fully understood. In the present study, we firstly took texture characteristics from the perspective of image analysis techniques into consideration to evaluate the role of colour and texture in fabric image preference. The results showed that, even though colour characteristics play an important role, the addition of texture features, lead to better predictive performance in the evaluation of fabric image preference using machine learning techniques.

The domain and range of the CIECAM16 forward transformation was numerically determined and visualized for CIE standard illuminants, using a linear programming approach that provides the gamuts and colour solids for optimum colours. The effect of the surround, adapting luminance, and luminance of the background on the range of the CIECAM16 forward transformation were individually analyzed, showing that their ranges increased when the surround changed from dark to dim or average, the adapting luminance increased, or the luminance of the background decreased. The proposed methodology for the determination and visualization of the domain and range of the CIECAM16 forward transformation can be used for any illuminant, as well as for CIECAM02, CAM16, CAM02-UCS and CAM16-UCS. The findings of this paper not only solve the long-term unresolved domain and range problems of the CIE colour appearance models, but also find applications in cross-media colour reproduction. Furthermore, it was also found that some non-CIE colours are inside the International Color Consortium Profile Connection Space (ICC PCS), and some CIE colours are not included in that space.

Online shopping is being more popular, which is a way that only images viewed on the displays can convey information for customers. The tactile properties of the fabrics are one of the most important characteristics that affect customers’ desire to purchase. People can only imagine the feeling of fabrics tactile by viewing the images when shopping online. In such situation, whether the perception of tactile properties from images, namely visual perception, is consistent with the actual touch perception has not been fully understood. The aim of this study is to investigate the tactile properties of fabrics by touch and view. 15 fabrics representing various textures were selected to evaluate the actual touch perception. Subsequently, images of these fabrics in flat and draped shapes were captured using a Sony DSLR camera respectively to evaluate the visual perception of tactile properties on a BenQ profession display. Four dimensional tactile properties, flexible-stiff, smooth-rough, soft-firm, spongy-crisp, were evaluated in two psychophysical experiments (fabric by touch and fabric by view) using the subjective ratings (1-9). Three set of data were obtained for each fabric: Tactile by touch. Tactile by viewing flat fabric images. Tactile by viewing draped fabric images. Their correlation was analysed and compared in this study. The results showed that when viewing fabric images, fabric shape (flat or draped) significantly affected the visual perception except for smooth-rough. Comparing these two shapes, the correlation between actual touch perception and visual perception of tactile properties was stronger using draped fabric images. It is also found that the correlation between actual touch perception and visual perception was the strongest for tactile properties of soft-firm, while the weakest for smooth-rough regarding the two shapes. Together, this study reveals an important role of shape in perceiving tactile properties visually, showing a consistent visual perception with actual touch perception.

To accurately reproduce preferred skin colour is an important goal in digital image colour reproduction. A psychophysical experiment was conducted to specify the preferred skin colour for different skin types. Ten original facial images were captured to cover different skin types, including Caucasian, Chinese, South Asian, African, different genders and different ages. For each original image, 49 rendered images, uniformly sampled within the skin colour ellipsoid in CIELAB colour space, were used to morph the skin colours. Thirty observers from each of three ethnic backgrounds, Caucasian, Chinese and South Asian, participated in the experiment to investigate ethnic differences. Ellipsoid models were developed to specify preferred skin colour regions and centres for each original image. These results can be used to improve skin colour reproduction of colour imaging products, for example on mobile phones, for different skin types.

Various facial colour cues (average/local skin colour, colour contrasts, colour variations, etc.) were identified as valid predictors of facial attractiveness. Conventional studies on single colour variables simplified the complex nature of attractiveness judgement on real human faces. However, predicting attractiveness from various colour cues is difficult due to the high number of candidate variables and their correlations. In this study, multivariate statistical techniques and machine learning (ML) algorithms were utilized to model the relationship between facial attractiveness and a large number of colour variables using Chinese samples. One hundred images of real human faces were used as the experimental materials, with the colour rigorously controlled to represent the naturally occurring facial colour variations in Chinese populations. Two separate attractiveness evaluation data were collected through psychophysical experiments as training and testing dataset, respectively. We proposed eight strategies for robust regression of the high-dimensional dataset based on three techniques: subset selection (forward, backward stepwise), dimension reduction (principal component regression, partial least-squares regression), and regularization (Ridge, Lasso, Elastic Net regression). Model performance was evaluated by the predictive accuracy, the goodness of fit, and the selection of colour predictors. Results showed the out-of-sample root-mean-square error for dimension reduction and regularization methods was better than the classical least-squares. The best ML algorithm predicted facial attractiveness within 0.67 points on a 7-point scale. Different predictors were selected depending on methods but several common predictors were revealed as important features including skin lightness, overall colour variation, and colour contrast around eyebrows. Here we evaluated statistical and ML algorithms for utilizing facial colour cues for attractiveness prediction based on realistic skin models. From the perspective of both well-predicting and interpretable, ML techniques with feature selection were recommended for attractiveness modelling. Our results also demonstrated the importance of colour to facial attractiveness which is comparable to those structural features.

The impact of colour characteristics on facial preference has gained much attention in recent years. With various colour characteristics being examined individually, it is unknown how these characteristics taken together would affect facial preference and what are the most important colour predictors in determining that preference. In this research, colour characteristics including average/local skin colour, skin colour variation, and facial colour contrast were measured using non-manipulated images of both real Caucasian and real Chinese faces. A rating study was conducted, using both Caucasian and Chinese observers, to obtain preference evaluations including facial attractiveness, perceived healthiness, and visual age. We first made a comprehensive examination of the relationship between the various colour characteristics and facial preferences. We then, using multiple regression analyses, evaluated and compared the importance of different facial colour characteristics in predicting each of the three separate preference attributes. On the one hand, our study revealed a moderate role for colour characteristics in determining facial preference within an evolutionary meaningful parameter space. Although the averaged skin colour of facial areas played a limited role, together with colour variation and contrast, there were stronger links between colour and facial preference than previously revealed. On the other hand, different facial colour cues were found to be utilized by different observers according to the different preference attributes they were accessing. Generally, Chinese observers tend to rely more heavily on colour cues to judge facial preference than Caucasian observers. The results highlighted the importance of examining various facial colour cues to obtain the full picture of colour predictors utilized in facial preference evaluation and also demonstrated the cultural difference between Caucasian and Chinese observers.

The digital archive of cultural heritage provides new opportunities for the protection of the cultural heritage and the development of online museums. One of the essential requirements for the digitization is to achieve accurate color reproduction. Taking the Imperial Chinese robes in the Qing Dynasty as an example, this study aims to develop a digital achieve system to digitize the robes using a high-end imaging system and accurately reproduce their color properties on a display. Currently, there has been very limited study focused on the color reproduction of silk fabrics or other textile materials. The conventional color management process using a traditional color chart, however, may not be suitable for the reproduction of silk fabrics because they have very high gloss. To address this difficulty, a unique “Qianlong Palette” color chart, consisting of 210 silk fabric samples, has been specifically produced for optimizing the color reproduction of silk fabrics and a color image reproduction system has been developed for the digitization and archiving of the clothing fabric for the royal court. Color characterization models using both the “Qianlong Palette” color chart and the traditional color chart, and different mapping methods, are compared and the model with highest accuracy used in a self-programmed interface for automatically processing textile images in the future. Finally, the digital archive system has been validated using six garments of silk fabric relics. The color differences after the color image reproduction are all less than 3.00ΔE*ab, indicating acceptable color reproduction of the system. The images after color reproduction have also been evaluated subjectively by experts from the museum and the results are considered satisfactory. Our results show that the newly designed “Qianlong Palette” color chart exhibits superior performance over the conventional color chart in effectively predicting the color of the silk fabrics. The self-programmed graphical user interface for image color management can serve as a powerful tool to truly reproduce the color of various silk fabric relics in museums in the future and digitally archive those valuable cultural relics for different uses.

The computation of perceived attractiveness from facial images has long been a research topic. Many models have been developed to predict the attractiveness of the face from the individual models that have been used to describe the geometry of the face (symmetry, golden ratio and neoclassical canons, according to artists from the Middle Ages, and a combination of the three). An experiment was conducted based on Oriental and South Asian ethnic groups, represented by Chinese and Pakistani facial images. Visual assessments of perceived attractiveness were carried out using a 6-point categorical scale and the results were used to derive a new set of facial feature ratios that maximized the perceived attractiveness of the two ethnic groups. The results were also used to develop a new polynomial model of attractiveness, and to test four existing models. The new model performed the best for Oriental faces. The new model was also best for South Asian faces together with the combined model. Ethnic group differences did not have a significant impact on the perceived attractiveness of the two groups. A set of new facial ratios for the two ethnic groups was determined to maximise attractiveness.