This article presents a framework for understanding, modeling, and computing color categories on the basis of knowledge from the color imaging science. One of the main assumptions advocated in this article is that the structure of color categories originates from the statistical structure of the perceived color environment that was observed throughout an individual's life. The perceived color environment can be modeled as color statistics of natural images in some perceptual and approximately uniform color space (e.g., the CIELUV color space). The process of color categorization can be modeled as the grouping of the color statistics by clustering algorithms (e.g., K-means). The proposed computational model enables one to predict the location, rank, and number of color categories. The model is examined on the basis of K-means clustering analysis of statistics of 630 natural images in the CIELUV color space. In general, the model predictions are consistent with data from psycholinguistic studies. The model might be applied in different areas of imaging science such as color quantization, image quality, and gamut mapping.

Images can be broadly categorized as belonging to one of two types of image states: unrendered and rendered. Images in an unrendered image state are directly related to the colorimetry of a real or hypothetical original scene. Images in a rendered image state are representations of the colorimetry of an output image, such as a print or a CRT display. Over the years, many device-dependent and device-independent color encodings have been used to represent images in both of these image states. This has resulted in a host of interoperability problems between various systems. There would be a significant advantage to standardizing on a small number of color encodings for the purposes of storage, interchange and manipulation of digital images. While the recently agreed upon sRGB color encoding specification is one important step towards such standardization, there is also a need to define standard color encodings that are not limited by the color gamut of any specific device. This article will describe a family of new color encodings that have been developed to address this need. A new color encoding specification known as Reference Output Medium Metric RGB (ROMM RGB) is defined for representing rendered or output-referred images, and a companion color encoding specification, known as Reference Input Medium Metric RGB (RIMM RGB), is defined for representing unrendered or scene-referred images.

Because many types of electronic imaging devices are now available, cross-media color reproduction technology has received widespread attention due to the need to provide accurate color stimuli for different devices. In the case of cross-media color reproduction between a monitor and a printer, RGB has to be converted into a device-independent color space in order to translate the color information between the two devices. Thereafter, gamut mapping is used to compensate for any gamut mismatch and device-independent colors have to be re-converted into output colors such as CMY control values for printing. For color conversion between device colors and device-independent colors, empirical representation using sample measurements is currently widely utilized. In the case of the printer, color samples are uniformly selected in the colorant space, printed as color patches, and then measured. However, because these color samples are not evenly distributed inside the printer gamut, the color conversion error is increased. Accordingly, this article introduces a color-sampling algorithm for a printer to reduce the error in color conversion, and the performance is analyzed via color conversion experiments using three conversion methods, regression, neural network, and interpolation.

A spatial gamut mapping technique is proposed to overcome the shortcomings encountered with standard pointwise gamut mapping algorithms by preserving spatially local luminance variations in the original image. It does so by first processing the image through a standard pointwise gamut-mapping algorithm. The difference between the original image luminance Y and gamut mapped image luminance Y' is calculated. A spatial filter is then applied to this difference signal, whose output is added back to the gamut mapped signal Y'. The filtering operation can cause some pixel colors that lie near the gamut boundary to be moved outside of the gamut, hence a second gamut mapping step is required to move these pixel colors back into the gamut. Finally, all pixels are processed through a color correction function for the output device, and rendered for that device. The algorithm is designed to reduce many of the artifacts arising from standard pointwise techniques. Psychophysical experiments indicated an observer preference for the proposed algorithm.

Mapping pigmentation in human skin is expected to give useful information in reproducing skin color and enhancing the ability to diagnose various skin disease. In this research, maps of melanin, oxy-hemoglobin and deoxy-hemoglobin in skin are estimated from multi-channel visible spectrum image by using an inverse optical scattering technique. In the inverse optical scattering technique, first of all, a forward model of optical scattering is built to simulate the spectral reflectance of skin. Changing the variable parameters in the forward model, we repeat the simulation until the simulated spectral reflectance matches with the spectral reflectance at each pixel of the multi-spectral image. The principle of the proposed estimation technique was confirmed by imaging the human forearm under conditions of the venous occlusion, the venous and arterial occlusion, and by imaging a slapped region of the human forearm.

We propose a method for extracting goniospectral information of three-dimensional objects from multiband images obtained under several illuminants and reproducing the objects under various kinds of illuminants. Using the dichromatic reflection model, goniophotometric information of both diffuse component and specular component is estimated from the images acquired under the illuminant from several illumination directions. On the other hand, spectral information is estimated from five band images using the minimum mean square error criterion. Experimental results using simple three-dimensional objects are presented to demonstrate the basic performance of the proposed method.

Printing books-on-demand is a new technology that is revolutionizing the book printing and publishing industry. One of the biggest bottlenecks in this process is the conversion of existing books into digital form. This typically involves digitization of original books through scanning, which is a slow and labor-intensive process. Careful attention must be paid to maintain the quality of the reproduced books and in particular of the images they contain. Halftoned image areas in the original books cause the most reproduction problems, as there is the potential that moiré patterns may form when these image areas are re-screened. In order to avoid these moiré patterns, it is necessary to detect the image areas of the document and remove the screen pattern present in those areas. In the past, we have presented techniques to perform these operations in the case of grayscale images. In this article, we extend these techniques to handle color images. We present efficient and robust techniques to segment a color document into halftone image areas, detect the presence and frequency of screen patterns in halftone areas and suppress the detected screens. Halftoned image areas are segmented by using a measure of image activity; image activity is low in text areas and high in halftoned areas. We use 2-D Fourier spectral analysis to identify the screen frequencies present. The screens are then suppressed by low-pass filtering. Our technique speeds up the conversion process of books to digital form, and overcomes quality problems in the reproduction of halftoned images.

The fundamental cause of the Yule-Nielsen effect, also called Optical Dot Gain, is the lateral scattering of light in paper. The lateral scatter of light can be described quantitatively by a point spread function, but a simpler model based on mean level scattering probabilities, P_ij, for scattering of light from region i to region j, has been used to describe the Yule-Nielsen effect in terms of the sum of three, rather than two, reflectance factors. The three reflectance factors are (1) the reflectance of the ink, R_i, (2) the reflectance of the paper, R_p, and (3) an apparent reflectance factor for a third component in the system, RCL. The nature and origin of the third component is modeled and shown to fit both tone and color behavior for an example halftone system.

The CIE system allows the specification of color matches for a standard observer using the color-matching functions (cmfs). However, the cmfs of an individual observer are different from those of the CIE within a finite range. This article describes the optimization of the cmfs of an individual observer based on metameric pairs using a variation method. This is a so much simplified method for estimating rough and ready cmfs of an individual observer in comparison with past experiments. The underlying assumption of the optimization is that the optimum cmfs will predict that the integrated cone responses of a metameric pair are equal. The feature of the proposed optimization method is that the color difference in a metamer pair can be optimized to 0 at a boundary condition in the variation method, and the smoothness of the modified cmfs results from the cost function of the least mean square of modified values in the variation method. The cost function of the variation method is generalized to consider the perception of color differences by the human visual system. Experiments using measured metamer spectral data demonstrate the validity of the proposed method. In the Supplemental Material (found on the IS&T website at <ext-link ext-link-type="uri" xlink:href="http://www.imaging.org">www.imaging.org</ext-link>, for no less than 2 years from the date of publication), theorems and characteristics are presented and proved to demonstrate the rigid theoretical background supporting the experimental validity.

We present two generalizations of the Williams–Clapper transform for converting between transmission and reflection densities of a color print. The first generalization allows for arbitrary incident angle, viewing angle, and index of refraction. The second generalization is to the geometry of an integrating sphere with the specular reflection either included or excluded. Our derivation also clarifies a potential source of confusion: Williams and Clapper had noted that, because of the cancellation of two factors, a reflection print with a perfectly transmitting gelatin layer (and a base of perfect reflectance) has the same brightness as it would have in the absence of the gelatin layer. We find that this cancellation is in fact only approximate. The approximate nature of the cancellation was not readily apparent because of the fortuitous closeness of the cancellation for the particular geometry and indices of refraction considered by Williams and Clapper. We also discuss efficient numerical implementation of the transform and outline an example of an application.