Color Research and Application最新文献

The random walk model for photon migration in turbid media is extended to predict reflectance spectra and CIELAB color for uncoated, glossy-coated, and matte-coated substrates. The model reproduces the known effect that a clear glossy layer increases saturation and reduces lightness, with very little change in hue. By comparing color of the uncoated, glossy coated and matte coated surfaces the model demonstrates that this change in color arises from (a) loss of surface-reflected light (specular component) and (b) longer path lengths (higher absorption) due to internal reflection by the coating. The model shows that the relative weights of these two factors depends on absorber concentration. The random walk model is reviewed: the model calculates the probability density for a photon's position after some number of steps within a turbid slab, and determines the reflectance of the slab as the probability flux through the slab surface.

In conceptual spaces, the distance between concepts is represented by a metric that cannot usually be expressed as a function of a few salient physical properties of the represented items. For example, the space of colors can be endowed with a metric capturing the degree to which two chromatic stimuli are perceived as different. As many optical illusions have shown, the color with which a stimulus is perceived depends, among other contextual factors, on the chromaticity of its surround, an effect called “chromatic induction”. Heuristically, the surround pushes the color of the stimulus away from its own chromaticity, increasing the salience of the boundary. Previous studies have described how the magnitude of the push depends on the chromaticity of both the stimulus and the surround, concluding that the space of colors contains anisotropies and inhomogeneities. The importance of contextuality has cast doubt on the practical or predictive utility of perceptual metrics, beyond a mathematical curiosity. Here we provide evidence that the metric structure of the space of colors is indeed useful and has predictive power. By using a notion of distance between colors emerging from a subjective metric, we show that the anisotropies and inhomogeneities reported in previous studies can be eliminated. The resulting symmetry allows us to derive a universal curve for the average chromatic induction that contains no fitting parameters and is confirmed by experimental data. The theory also predicts the magnitude of chromatic induction for every possible combination of stimulus and surround, demonstrating that, at least in the case of colors, the metric captures the symmetries of perception and augments the predictive power of theories.

Traditional Chinese color still lies in conceptual color terms due to the lack of quantitative measures and methods. Consequently, a standardized technical system has not been established, which has hindered proper regulation, application, and research of traditional Chinese colors in contemporary context. To address these limitations and pave the way for a quantified system of traditional Chinese color, samples of traditional color names were collected and organized, and the meta elements of color values, gamut, and hierarchical relationships of color terms were quantitatively analyzed. The quantification schemes and the hierarchical relationship model of traditional Chinese color terms were demonstrated. The project prospectively laid a groundwork for comprehensive analyzing traditional Chinese color in a digital technology environment, by proposing fundamental methods for establishing a quantitative system of traditional Chinese color.

Perceived colorfulness (or color saturation) of a new high-brightness color stimuli dataset was measured in a psychophysical scaling experiment. Stimuli created with a 7-primary LED system covered a broad range of luminance from 50 to 2000 cd/m³ and reached chromaticities beyond Rec. 2100. Experimental results reveal that previous color appearance models, such as CIECAM16 and CIELAB, do not accurately describe the measured colorfulness results, but predictions can be improved with simple power functions. Testing shows that applying a simple model results in statistically significant improvements in the performance of the predictors. This work will not only help understand the color perception of future HDR displays and advanced lighting system with narrow-band primaries, but also contribute to future improvements to CAMs.

Multi-channel LED luminaires are becoming more popular and offer a large degree of freedom for retailers when illuminating their products. Inevitably, this raises also the need to select the most appropriate illumination spectrum for any of the presented objects. In this paper, a visual experiment was designed to identify the most attractive appearance of a set of familiar objects under varying illumination conditions. A selection of vegetables and fruits from 5 hue bins were illuminated with a number of illuminants at a predefined chromaticity and correlated color temperatures (CCT) of 3000 K, 4000 K and 6500 K, with a targeted illuminance of 500 lux, strategically selected from the entire range of metamers, which can be generated by the multi-channel luminaire. Observers were asked to select the most attractive appearance of the presented object in a paired comparison experiment. The results illustrate that optimum spectra that render an object more attractive than other spectra for a statistically meaningful number of observers can be defined for each particular illumination chromaticity. An analysis of the results suggests that the TM-30 local chroma shift ($�\; ��\; �R_{�\; �\mathrm{cs}�\; �,�\; �h�\; �j�}��$) was the best predictor of attractiveness for the objects involved in the study.

Various simulation methods of color appearance for dichromats or anomalous trichromats have been proposed over the years. To further improve the performance of the simulation model and extend the application range to both dichromats and anomalous trichromats, we have proposed a simulation model of cone fundamentals specifically designed for individuals with red–green type color vision deficiency (CVD) based on the CIE 2006 physiological observer model. By utilizing the simulated cone fundamentals, it becomes possible to predict the color appearance of real scenes and digital images for CVD. The fundamental premise of the new model is rooted in the hypothesis that CVD arises from a shift in the peak wavelength of the photopigment absorption spectrum of the L- or M-cone. Instead of simply maintaining the waveform without alteration as observed in prior studies, we altered waveforms of the absorption spectra of anomalous L-/M-cone photopigments when adjusting their peak wavelengths. Regarding different shapes in the absorption spectrum between the L- and M-cones, the absorption spectrum of the anomalous L-/M-cone was obtained by combining the peak wavenumber shift and linear interpolation of spectral quantal absorption curves between L- and M-photopigments in the wavenumber domain. The performance of the proposed model was substantiated through experimental validation by the pseudoisochromatic plates and Farnsworth Munsell 100 Hue test (FM-100). The findings revealed a high level of consistency between the model prediction and the actual perception reported by individuals with CVD.

Hue can be described with two types of scales for hue discrimination and hue appearance purposes. A fundamental and physiologically plausible model, called FHS for Fundamental Hue Scales, with both types of hue scales, built directly from cone fundamentals, was introduced in the previous study. In the current study, the FHS model was improved. The first step of the FHS model is a simple conversion of the LMS cone responses into the opponent axes. The opponent axes can be converted into a hue discrimination scale using a rotation matrix that is generated based on five principal hues, or they can be converted into a hue appearance scale using a rotation matrix based on four unique hues. The hue discrimination scale is called $�\; ��\; �{\mathrm{FHS}}_h��$, and the hue appearance scale is called $�\; ��\; �{\mathrm{FHS}}_H��$. The proposed hue model was evaluated in terms of linearity, spacing, and hue quadrature using traditional datasets including the Munsell, NCS, Hung–Berns, and Ebner–Fairchild. In addition to the available datasets, three experiments were completed to evaluate the FHS model for stimuli with higher saturation and higher luminance levels. In the first experiment, constant hue surfaces of eight hue angles were found to assess the linearity of the hue scales. In the second experiment, the spacing between 10 pairs of constant hue surfaces was examined. The third experiment was conducted to evaluate the hue quadrature of intermediate hues. Results showed that the FHS is successful in terms of linearity, spacing, and hue quadrature prediction. The FHS hue scales are mathematically very simple, physiologically and perceptually plausible, and directly based on cone fundamentals to allow easy generalization to individual observers and perform at least as well as more complicated models.