Search

This paper describes the use of a weighted principal component analysis (PCA) method for camera spectral sensitivity estimation. A comprehensive set of spectral sensitivities of 111 cameras was collected from four publicly available databases. It was proposed to weight the spectral sensitivities in the database according to the similarities with those of the test camera. The similarity was evaluated by the reciprocal predicted errors of camera responses. Thus, a set of dynamic principal components was generated from the weighted spectral sensitivity database and served as the basis functions to estimate spectral sensitivities. The test stimuli included self-luminous colors from a multi-channel LED system and reflective colors from a color chart. The proposed method was tested in both the simulated and practical experiments, and the results were compared with the classical PCA method, three commonly used basis function methods (Fourier, polynomial, and radial bases), and a regularization method. It was demonstrated that the proposed method significantly improved the accuracy of spectral sensitivity estimation.

With the advancement of imaging technology, high dynamic range (HDR) images can now be captured and displayed to produce realistic effects. Tone mapping operators (TMOs) are used to map HDR radiance to the displayable range. A reliable TMO would play a significant role in the accurate reproduction of HDR scenes. The present study aimed to establish an image quality metric based on external references to evaluate various TMOs. Two psychophysical experiments were conducted to develop reference images and to investigate the performance of TMOs. In experiment 1, a set of high quality reference images was developed by rendering image features in terms of contrast, sharpness, and colorfulness, to achieve good rendering for each image. The images were used as reference in experiment 2 to evaluate the performance of 14 TMOs using a six-point categorical judgment method. The TMOs were evaluated using four scales, i.e., contrast, sharpness, colorfulness, and overall performance. The hierarchical relationship among TMOs was established. The results were further compared with previous studies, and high correlation was found between the current experiments and previous studies.

Predicting the quality of natural images without using a reference image has always been a challenging task. Numerous approaches have been proposed in the past, but they mainly focused on spatial and frequency domain degradations like blur, noise, and compression. Image quality metrics (IQMs) in literature perform with quite a high accuracy for such types of degraded images. However, their performances are not good on the images modified in the color domain. In this study, psychophysical experiments were conducted to assess the quality of the color domain images. A new dataset was developed for this purpose. Additionally, a second dataset consisting of color domain modified images from the three previously published datasets were used in the psychophysical experiments. The newly developed dataset was then used to develop three IQMs based on absolute values, relative values, and statistical analysis of image color appearance attributes. Their performances were then evaluated together with five spatial domain IQMs from the literature using cross-database evaluation methodology. The results showed that the color-domain IQMs outperformed the other models. The absolute and relative attributes-based models, when combined, achieved the best performance. The present results suggest that more effort is needed to improve the performance of color domain IQMs for image quality estimation.

With the emerging demand for wide-color-gamut displays, an issue has been raised in which the commonly used color difference formulae or uniform color spaces that were derived based on the data produced in the relatively smaller color gamut could be unreliable for predicting color differences in the highly saturated color regions. A psychophysical experiment was carried out for evaluating color difference at a luminance level of 310 c d / m 2 on a wide-color-gamut display with an approximate DCI-P3 color gamut. Twelve color centers were selected to cover the entire gamut boundary. There were 192 pairs of samples over 12 color centers judged by 18 observers using the greyscale psychophysical method. The data set was used to test the performance of six uniform color spaces and color difference equations, CIELAB, CIEDE2000, CAM02-UCS, J z a z b z , I C T C P , and n I C T C P , a newly revised I C T C P formula. The color discrimination ellipses were used to test local and global uniformity of color spaces and compared with previous studies. The results revealed that all formulae improved their performance to have a mean lightness parametric factor of about 0.5. CAM02-UCS significantly outperformed the others in overall, local, and global uniformity. The high-quality visual data set is recommended to evaluate or to derive color difference formulae for WCG applications in the future.

Hue linearity is an important property of uniform color spaces such that hues perceived to be similar should be located on a straight line, an iso-hue line, in that space. Previously derived hue linearity data only cover a limited color gamut. Two new psychophysical experiments were conducted that used a wide color gamut (WCG) and high dynamic range (HDR) display to extend the color range using both hue matching and unitary hue estimation methods. The new data were used to evaluate the CIELAB, CAM16-UCS, IPT, and J z a z b z uniform color spaces. The experimental results indicated that IPT and J z a z b z outperformed the other two, especially in the blue region. The same method was used to test these spaces using the other published data sets. The results from different data sets gave similar results. Finally, all results were combined to form a normalized data set to represent the data under HDR and WCG display conditions. Furthermore, the four unitary hue data can be used to develop or refine color appearance models.

All color-difference formulas are developed to evaluate color differences for pairs of stimuli with hairline separation. In printing applications, however, color differences are frequently judged between a pair of samples with no separation (NS) because they are printed adjacently on the same piece of paper. A new formula, ΔE NS , has been developed for pairs of stimuli with NS. An experiment was conducted to investigate the effect of different color-difference magnitudes using sample pairs with NS. 1012 printed pairs with NS were prepared around 11 CIE recommended color centers. The pairs, representing four color-difference magnitudes of 1, 2, 4, and 8 CIELAB units were visually evaluated by a panel of 19 observers using the gray-scale method. Comparison of the present data based on pairs with NS, and previously generated data using pairs with hairline separation, showed a clear separation effect. A new color-difference equation for the NS viewing condition (ΔE NS ) is proposed by modifying the CIEDE2000 formula. The separation effect can be well described by the new formula. For a sample pair with NS, when the CIEDE2000 color difference is less than 9.1, a larger color difference leads to a larger lightness difference, but has no effect on the chromatic difference. When the CIEDE2000 color difference is greater than 9.1, the effect is the opposite. The new formula is recommended for future research to evaluate its performance in appropriate applications.

Memory colors generated continuous interest in the color community. Previous studies focused on reflecting color chips and color samples in real scenes or on monitors. The cognitive effect of culture was rarely considered. In this paper, we performed a comprehensive investigation of the long-term memory colors of 26 familiar objects using the asymmetric color matching method among Chinese and German observers on a display. Three experiments were conducted to evaluate the variations introduced by culture, context-based gray image, and initial matching color. Memory colors of important objects were collected and representative memory colors were quantified in terms of CIELAB L * , a * , and b * values. The intra- and inter-observer variations were analyzed by mean-color-difference-from-mean values and chromatic ellipses. The effects of different cultural groups and experimental settings were also shown.

Recent advances in bidirectional reflectance distribution function (BRDF) acquisitions have provided a novel approach for appearance measurement and analysis. In particular, since gloss appearance is dependent on the directional reflective properties of surfaces, it is reasonable to leverage the BRDF for gloss evaluation. In this paper, we investigate gloss appearance from both soft metrology and hard metrology. A psychophysical experiment was conducted for the gloss assessment of 47 neutral-color samples. In the evaluation of gloss perception from gloss meter measurements, we report several ambiguous correspondences in the medium gloss range. In order to analyze and explain this phenomenon, the BRDF was acquired and examined using a commercial BRDF measuring device. With an improved correlation-to-visual perception, we propose a two-dimensional gloss model by combining a parameter, the standard deviation of the specular lobe, from Ward's BRDF model with measured gloss values.

Whiteness is an important colorimetric characteristic for surface colors. Currently, the CIE whiteness formula is widely used to characterize the whiteness of a surface in industry, whose limitations have been well documented. In this study, the whiteness appearance of 55 textile samples [43 non-fluorescent whitening agents (FWAs) and 12 FWA samples] were evaluated under six lighting conditions, comprising three levels of correlated color temperature (CCT) (i.e., 3000, 4000, and 6500 K) and two levels of UV radiation (i.e., zero and high), by 12 observers with normal color vision. The results indicated that the CIE whiteness formula with the CAT02 chromatic adaptation transform and the optimized CIE whiteness formula had the best performance in predicting the whiteness of surface colors, regardless of whether FWAs are contained, among the six formulas.