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.

In this field study, a series of psychophysical tests were conducted to investigate the impact of indoor lighting on students’ visual perception and cognitive performance. A typical classroom of Wuhan University (China) was fitted with tubular light-emitting diode (LED) sources and LED panel sources in two experiments, respectively. Under the two lighting environments, seventy-nine college students were invited to complete a group of visual tests, which included colour preference evaluations of fruit and vegetables and skin tone, perceptual judgement on the atmosphere of the lighting environment, a reading comfort assessment concerning different paper colours, a Karolinska Sleepiness Scale (KSS) test quantifying alertness, and the Anfimov test of attention (also concerning paper colours). These tests were conducted twice, before and after a two-hour self-study under each lighting environment, with the aim of investigating the impact of visual fatigue on visual perception and cognitive performance. It was found that the influence of indoor lighting was significant on skin preference and atmosphere perception, while no measured effects of lighting were observed on participant’s alertness and attention. Meanwhile, the impact of visual fatigue was also found to be insignificant in this case. Interestingly, paper colour, rather than indoor lighting, was found to have a significant impact on the visual comfort of text reading. In addition, degree of proficiency significantly influenced the proofreading speed and accuracy of the subjects the in Anfimov test, which we believe should be taken into consideration when implementing similar tests in follow-up studies. The preliminary findings of this field study should provide a deeper understanding of how changes in classroom lighting contribute to visual perception and cognitive performance of occupants.

The current color-difference formulas were developed based on 2D samples and there is no standard guidance for the color-difference evaluation of 3D objects. The aim of this study was to test and optimize the CIELAB and CIEDE2000 color-difference formulas by using 42 pairs of 3D-printed spherical samples in Experiment I and 40 sample pairs in Experiment II. Fifteen human observers with normal color vision were invited to attend the visual experiments under simulated D65 illumination and assess the color differences of the 82 pairs of 3D spherical samples using the gray-scale method. The performances of the CIELAB and CIEDE2000 formulas were quantified by the STRESS index and F-test with respect to the collected visual results and three different optimization methods were performed on the original color-difference formulas by using the data from the 42 sample pairs in Experiment I. It was found that the optimum parametric factors for CIELAB were $k_L$ = 1.4 and $k_C$ = 1.9, whereas for CIEDE2000, $k_L$ = 1.5. The visual data of the 40 sample pairs in Experiment II were used to test the performance of the optimized formulas and the STRESS values obtained for CIELAB/CIEDE2000 were 32.8/32.9 for the original formulas and 25.3/25.4 for the optimized formulas. The F-test results indicated that a significant improvement was achieved using the proposed optimization of the parametric factors applied to both color-difference formulas for 3D-printed spherical samples.

In this study, an image‐based measurement system was developed for human facial skin colour, involving the development of a digital imaging system, collection of facial skin colour from 60 human subjects, generation of different colour characterization models, and performance evaluation. The factors that affect facial skin colour characterization, including different training datasets (two colour charts and the collected facial skin colour dataset), mathematical mapping methods (linear transformation, polynomial regression, root‐polynomial regression and neural network) and camera image formats (JPG and RAW), were investigated and quantified not only by the conventional method of CIELAB colour difference, but also two newly introduced measures, facial colour contrast and skin colour gamut. The results indicate that the RAW image format for camera digital signals gave a more stable performance than the JPG format images, and the higher order polynomial regression with good predictive accuracy in terms of CIELAB colour difference did not perform well for the whole facial image. It is suggested to evaluate the model performance using the colour of both specific facial positions and the overall facial skin colour. Our comparative analysis in this study provides useful guidance for determining the colour characterization model for facial skin.

An improved spectral reflectance estimation method was developed to transform captured RGB images to spectral reflectance. The novelty of our method is an iteratively reweighted regulated model that combines polynomial expansion signals, which was developed for spectral reflectance estimation, and a cross-polarized imaging system, which is used to eliminate glare and specular highlights. Two RGB images are captured under two illumination conditions. The method was tested using ColorChecker charts. The results demonstrate that the proposed method could make a significant improvement of the accuracy in both spectral and colorimetric: it can achieve 23.8% improved accuracy in mean CIEDE2000 color difference, while it achieves 24.6% improved accuracy in RMS error compared with classic regularized least squares (RLS) method. The proposed method is sufficiently accurate in predicting the spectral properties and their performance within an acceptable range, i.e., typical customer tolerance of less than 3 DE units in the graphic arts industry.