To evaluate and optimally design spectral sensitivity functions for color input devices, a metric that incorporates practical, significant requirements is desired. The candidate metrics are Vora–Trussell's μ-factor, a metric based on geometrical difference, and the proposed Unified Measure of Goodness, or UMG, which simultaneously considers the imaging noise and its propagation, colorimetric reproduction accuracy and multi-illuminant color correction. A systematic approach is presented to searching for an optimal set of spectral sensitivity functions from among the complete combinations of the given filter components. Comparative computation results show that μ-factor is not a competent metric for the optimal design of camera spectral sensitivity functions while UMG is able to pick out the optimum successfully. Furthermore, the ultimate optimal set has been obtained by selecting the set with highest μ-factor value from the sub-optimal collection obtained with UMG. This hierarchical approach comprehensively considers the advantages of both quality metrics. The candidates of the optimal sets based on the given filter components are experimentally tested and presented in the end of the article.

Spectral reflectances of various parts of human faces from various ethnic races were measured as part of experiments on spectral imaging for human portraits. Principal components analysis (PCA) was applied to the spectral reflectances from the various races, and a variety of face parts. The first three principal components explain about 99.8% of cumulative contribution of variance of spectral reflectances for each race and each face part, and for all races as well. Color differences of spectral reconstruction either for individual races and all races or for individual face parts based on different sets of principal components were estimated. The results indicate that, when using three basis functions and under D₅₀ illumination, the basis functions based only on spectra of Pacific–Asian subjects will provide the best overall color reproduction. However, from a spectral matching point of view, three basis functions based on all spectra will provide the best spectral reproduction with minimum overall mean value of metameric indices. More analyses were applied to spectral reflectances of human facial skin from different sources and their corresponding spectral reconstruction based on different sets of principal components. Those results provide practical suggestions for imaging, or spectral imaging, system design, especially imaging systems for human portraiture.

This article proposes an adaptive nonlinear quantization method for multispectral image compression. When linear scalar quantization is applied for multispectral image compression, extremely large error is perceived in low-luminance colors due to the nonlinear phenomenon of human vision. In the proposed method, quantization tables are switched pixel by pixel depending on the corresponding luminance. The switching rule is determined according to the relationship between the luminance and the error in the uniform color space. As a result, distribution of the error in the uniform color space can be equalized and the error in the low-luminance pixels is suppressed. Experimental results using a 16-band multispectral image of an oil painting shows the effectiveness of the proposed method.

A chroma scaling psychophysics experiment was conducted using a CRT in a dark surround. The experiment consisted of constant hue and lightness IPT step ramps on a uniform background. Each hue step ramp was displayed on an achromatic, a medium and a high chroma background. The results clearly show that chroma scaling is dependent on the chroma of the background. For the medium chroma backgrounds a modest crispening effect is evident. The results also show that chroma scaling on achromatic and high chroma backgrounds differ considerably. The data for the achromatic backgrounds are used to compare C*ab, CIECAM97s C, the revised CIECAM97s chroma, C_F and CIECAM02 C. One phase of the LUTCHI data is also used to verify the results from the visual experiment. CIECAM02 C and C_F both provide good fits to the different data sets relative to C*ab. Furthermore, CIECAM02 C and CF both have considerably smaller intercepts for linear fits of the scale versus the correlate relative to CIECAM97s C.

The final color quality of overhead projection transparencies depends on the transparency print, the projector, the projection screen, and the viewing environment. For color calibration, there are commercial measurement instruments capable of transparency sample spectral transmittance and transmittance color measurement. However, projection stray light, sample scattering for samples printed with EP technologies, and projector design parallax limitation as in reflective type projectors makes using directly measured transmittance data to represent the projected colors inadequate. In this article, we report our effort and findings on overhead projection transparency color measurement. We devised an automated measurement system based on a spectroradiometer to measure multiple projected colors automatically; the results are compared with that obtained by direct sample transmittance measurement with both diffuse illumination and collimated illumination. We concluded that for superior overhead projection transparency color calibration, projected colors should be measured to compensate for stray light, sample scattering, and projector optical design limitations.

Relative amounts of pigments on an overhead projector (OHP) sheet printed by laser printer can be estimated by using spectral transmittance at each pixel of an optical micrograph, and the multi-layered pigments also can be separated into each component. The transmittance spectra were estimated from multiband images by the Wiener estimation method. In the experiment, relative amounts of pigments printed by one or two kinds of color pigments were estimated, and overlapping magenta and yellow pigments were separated into each category. The result was accurate as compared with separation based on RGB values.

The Pareto-optimal approach to color management, presented previously by the authors, is further developed to allow for direct conversion into CMYK with complete user control over gray component replacement (GCR). The Pareto-optimal formulation unifies a number of strategies for transforming image data into CMYK: specification of arbitrary GCR, conversion using only chromatic inks, and conversion using at most two chromatic inks and black ink. Use of the black printer is analyzed in terms of extending the CMY gamut and replacing chromatic inks. The program NeuralColor is used to implement the Pareto-optimal formulation, providing data for in-depth analysis of the various conversion methods. NeuralColor accurately models the transformation from CIELAB to CMYK using artificial neural networks. Prints obtained using NeuralColor are accurate within 2 to 4 ΔE^*_ab across all levels of GCR.

The current paper proposes a modified jointly-blue noise mask (MJBNM) method using the S-CIELAB color measure. Based on an investigation of the relation between the pattern visibility and the chromatic error of a blue noise pattern, a halftoning method is proposed that reduces the chromatic error, while preserving a high quality blue noise pattern. Although the jointly-blue noise mask (JBNM) method provides a visually pleasing pattern for single and multiple color planes, the halftone outputs of a JBNM mask exhibit a higher chrominance error. Accordingly, to reduce the chrominance error, the low-pass filtered error and S-CIELAB chrominance error are both considered during the mask generation procedure and calculated for single and combined patterns. Using the calculated low-pass filtered error, the patterns are then updated by either adding or removing dots from the multiple binary patterns. Finally, the pattern exhibiting the lower S-CIELAB chrominance error is selected. Experimental results demonstrated that the proposed algorithm can produce a visually pleasing halftoned image with a lower chrominance error than the JBNM method.