Color perception of an object is tremendously influenced by the spectral power distribution (SPD) of a light source. Therefore, it is of great significance to seek for an optimal SPD to enhance the color contrast of a biological specimen in medical lighting. Meanwhile, considering that color rendering property and illuminance are of importance to lighting, with a multichannel LED light source and a differential evolution algorithm dealing with the constraints of color rendering and illuminance, a contrast enhancement method based on maximizing CIEDE2000 color difference (ΔE00*) was proposed in this study. An illuminance level of 500 lx was first verified as the desired illuminance. Afterward, the color contrasts of six groups of color patches were optimized under four different constraints of the Commission Internationale de l’Eclairage (CIE) general color rendering index (Ra), i.e., Ra ≥ 0, Ra ≥ 60, Ra ≥ 80, and Ra ≥ 90, manifesting their significant color contrast enhancement under the optimized light sources compared with those under the simulated CIE A, D50, D65, and D75 light sources. Further, two biological samples, pork tissues and porcine heart, were optimized under the four constraints of Ra. The results show that even in the case of Ra ≥ 90, the optimized ΔE00* values for the two specimens are, respectively, 4.13 and 3.20 higher than those under the D75 light source. More importantly, the developed method also provides a flexible lighting solution to color contrast enhancement.
In multispectral imaging, it is necessary to select a reduced number of filters to balance the imaging efficiency and spectral reflectance recovery accuracy. Due to the combined effect of filters and light source on reflectance recovery, the optimal filters are influenced by the employed light source in the multispectral imaging system. By casting the filter selection as an optimization issue, the selection of optimal filters corresponding to the employed light source proceeds with respect to a set of target samples utilizing one kind of genetic algorithms, regardless of the detailed spectral characteristics of the light source, filters, and sensor. Under three light sources with distinct spectral power distributions, the proposed filter selection method was evaluated on a filter-wheel based multispectral device with a set of interference filters. It was verified that the filters derived by the proposed method achieve better spectral and colorimetric accuracy of reflectance recovery than the conventional one under different light sources.
An automatic geometrical calibration approach has been developed to calibrate a multiprojector-type light field (LF) display automatically and accurately. The calibration framework based on image mapping is detailed, which transfers the calibration of three-dimensional (3-D) scene into the calibration of two-dimensional image in the diffuser interface. A multiprojectors-type LF display prototype is applied to implement the experimental calibration. Comparison results of the reconstructed 3-D scene before and after calibration show that a better overall performance is obtained through the proposed calibration approach.
Large-scale psychophysical experiments are carried out on two types of mobile displays to evaluate the perceived image quality (IQ). Eight perceptual attributes, i.e., naturalness, colorfulness, brightness, contrast, sharpness, clearness, preference, and overall IQ, are visually assessed via categorical judgment method for various application types of test images, which were manipulated by different methods. Their correlations are deeply discussed, and further factor analysis revealed the two essential components to describe the overall IQ, i.e., the component of image detail aspect and the component of color information aspect. Clearness and naturalness are regarded as two principal factors for natural scene images, whereas clearness and colorfulness were selected as key attributes affecting the overall IQ for other application types of images. Accordingly, based on these selected attributes, two kinds of empirical models are built to predict the overall IQ of mobile displays for different application types of images.
The image quality of two active matrix organic light emitting diode (AMOLED) smart-phone displays and two in-plane switching (IPS) ones was visually assessed at two levels of ambient lighting conditions corresponding to indoor and outdoor applications, respectively. Naturalness, colorfulness, brightness, contrast, sharpness, and overall image quality were evaluated via psychophysical experiment by categorical judgment method using test images selected from different application categories. The experimental results show that the AMOLED displays perform better on colorfulness because of their wide color gamut, while the high pixel resolution and high peak luminance of the IPS panels help the perception of brightness, contrast, and sharpness. Further statistical analysis of ANOVA indicates that ambient lighting levels have significant influences on the attributes of brightness and contrast.
Proc. SPIE. 6033, ICO20: Illumination, Radiation, and Color Technologies
KEYWORDS: Mathematical modeling, Principal component analysis, Statistical analysis, Detection and tracking algorithms, Scanners, Reflectivity, Color difference, Neural networks, Analytical research, RGB color model
Spectral characterization technique has a prominent advantage that it does not suffer from the problem of metamerism in comparison with Colorimetric characterization methods. PCA (Principle Component Analysis) is an important and useful mathematical method for data reduction, in which a set of spectra, so-called statistical colorants, can be derived from spectral properties of a large set of samples. The spectral reflectance of the color, an admixture of these statistical colorants, can be represented by approximately linear addition of their spectral reflectances. In this paper, a new method for spectral characterization of a flat panel color scanner using PCA method was proposed. Firstly, the PCA algorithm was applied to estimate the spectral reflectance of the statistical colorants on the color targets scanned, and then the colorant scalars were calculated. Secondly, the relationship between the colorant scalars and the scanner RGB signals was built using BP (Back Propagation) neural network. The scanner was characterized also using polynomial regression model and BP neural network directly between scanner RGB values and divice-independent tristimulus values. The experiment results showed that the spectral characterization using PCA method was more accurate than the polynomial regression model and similarly accurate as the direct neural network method but more useful because of the accurate spectral reflectance estimation ability.
CRT color gamut boundaries can be determined by two steps workflow. Firstly, the display should be calibrated with the method recommended by CIE to characterize the relationship between CIE tristimulus values and DAC values. The nonlinear relationship of each electronic channel between the color of the radiant output of CRT displays and the digital DAC values can be characterized accurately with GOG model using parameters of gain, offset, and gamma. Secondly, color gamut boundary can be determined using a fast and accurate algorithm. Generally, in a color space, any chosen degree of lightness will reduce that space to a plane. The color gamut on this equal-lightness plane can be transformed into RGB DAC value space. Since locations on the edges and surfaces of RGB DAC value space will correspond colors with relatively high saturation, the boundary of the curved surface in RGB DAC value space can be quickly computed for certain lightness. The accurate color gamut is obtained by mapping this boundary over to such a perceptual color space as CIELAB or CIELUV uniform color space. The key issue of this algorithm is to compute the equal-lightness curved surface in RGB DAC value space. The resolution of device gamut description depends on the number of segments that the lightness axis is separated into in the perceptual color space.
A psychophysical experiment was carried out with the method of constant stimuli using CRT-generated color samples. The experimental results at the five CIE color centers of Gray, Red, Yellow, Green, and Blue were satisfactorily described by chromaticity ellipses as equal color-difference contours in the CIELAB space. The CIEDE2000 formula, together with other two advanced color-difference formulae, CMC and CIE94, and the basic CIELAB equation, were tested using the visual data obtained from the present experiment. The comparisons between color differences, ?E, predicted by individual formulae and the corresponding visual scales, ?V, were carried out in terms of PF/3 measure. With their original forms, i.e. kL=kc=kH=l, or with their optimized kL values, the CIEDE2000 outperformed others for the combined dataset under the viewing condition in this study. Furthermore, the visual data at Blue center were well predicted by CIEDE2000 with an obviously better accuracy than other color-difference formulae. This confirms that the rotation item involved in the CIEDE2000 equation effectively improves the uniformity and predicting performance for the color differences in the blue region.
A technical approach and system scheme for fast high- precision spectral measurement of color appearance are proposed. This system is equipped with the modern self- scanning photodiode array devices as the photodetectors, the pulsed xenon lamp as the light source, and with the dual light paths and multichannel configuration. The related function modules of the instrument are designed. As the result of such a photo-electric arrangement, the measurement accuracy is improved a lot and the repeatability is also very satisfactory.