A linear interpolation method is applied for reconstruction of reflectance spectra of Munsell as well as ColorChecker SG color chips from the corresponding colorimetric values under a given set of viewing conditions. Hence, different types of lookup tables (LUTs) have been created to connect the colorimetric and spectrophotometeric data as the source and destination spaces in this approach. To optimize the algorithm, different color spaces and light sources have been used to build different types of LUTs. The effects of applied color datasets as well as employed color spaces are investigated. Results of recovery are evaluated by the mean and the maximum color difference values under other sets of standard light sources. The mean and the maximum values of root mean square (RMS) error between the reconstructed and the actual spectra are also calculated. Since the speed of reflectance reconstruction is a key point in the LUT algorithm, the processing time spent for interpolation of spectral data has also been measured for each model. Finally, the performance of the suggested interpolation technique is compared with that of the common principal component analysis method. According to the results, using the CIEXYZ tristimulus values as a source space shows priority over the CIELAB color space. Besides, the colorimetric position of a desired sample is a key point that indicates the success of the approach. In fact, because of the nature of the interpolation technique, the colorimetric position of the desired samples should be located inside the color gamut of available samples in the dataset. The resultant spectra that have been reconstructed by this technique show considerable improvement in terms of RMS error between the actual and the reconstructed reflectance spectra as well as CIELAB color differences under the other light source in comparison with those obtained from the standard PCA technique.
Widely varying estimates of the number of discernible object colors have been made by using various methods over the past 100 years. To clarify the source of the discrepancies in the previous, inconsistent estimates, the number of discernible object colors is estimated over a wide range of color temperatures and illuminance levels using several chromatic adaptation models, color spaces, and color difference limens. Efficient and accurate models are used to compute optimal-color solids and count the number of discernible colors. A comprehensive simulation reveals limitations in the ability of current color appearance models to estimate the number of discernible colors even if the color solid is smaller than the optimal-color solid. The estimates depend on the color appearance model, color space, and color difference limen used. The fundamental problem lies in the von Kries-type chromatic adaptation transforms, which have an unknown effect on the ranking of the number of discernible colors at different color temperatures.
An accurate colorimetric characterization of digital still cameras (DSCs) is vital to any high-quality color-reproduction system. However, achieving a perfect relationship between DSC responses and input spectral radiance is not practically easy, even when they have a reasonable linear relationship. In this research, we investigated differences in capturing geometries as a source of nonlinearity in camera characterization workflows. This nonlinearity can be corrected using a physical model describing the spectrophotometric changes according to illumination/capturing geometries. We introduced a model based on the Saunderson equation as an approach to predict surface properties suitable for paint layers in different geometries. According to the results, the Saunderson surface correction successfully compensated for the dissimilarities among spectrophotometric and spectroradiometric measurements, regardless of the capturing and lighting geometries. The model was also used for characterizing digital still cameras using matte, semi-glossy and glossy color targets as training datasets. The Saunderson-based models improved the transformation matrix for different geometries compared to conventional methods. Also, the results confirmed the validity of a simpler derivation of the Saunderson surface correction based on linear matrix operations. d
A new model of turbid medium theory is proposed that combines the best parts of the opaque forms of Kubelka‐Munk single and two constant models. This model introduces an impurity index, a spectrally nonselective scattering coefficient for each chromatic component. The new model is shown to have a convex, linear dependence on colorant concentration so that it can be used for colorant identification in high‐resolution images of works of art while offering an extension to the single constant model that can predict the absorption and scattering of paints, both in mixture and pure (masstone) forms. The model was tested and validated using 28 matte acrylic dispersion paints, of the type used in modern painting. These paints had a wide range of absorption and scattering characteristics. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 308–315, 2017
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