The principles of spectral color reproduction, as exemplified by the Lippmann method, and of trichromatic color reproduction, as used by presentday systems, are reviewed. Colorimetry is also based on trichromatic principles, and provides the basis for the quantitative evaluation of color reproduction in television, photography, and printing. Being metameric, these reproductions are affected by changes in illuminant and observer. They are also sensitive to changes in their viewing conditions; these changes can be represented by color vision models, one of which is briefly described.

What color is it? This is a deceptively simple question with a surprisingly complex answer. Color is thought of in many ways. It can be a certain kind of light or material, its effect on the human eye or the perceived effect in the mind of the viewer. The description of a color can evolve from instrumental measurement or from human visual assessment and in turn, such data can be further embellished by the human observer in the communication of color information. Color is also strongly affected by the context in which it is viewed with surrounding colors having a marked effect on perceived sensation. As a result, the accurate description of color is often problematic.Over the years, numerous Color Spaces have evolved to facilitate the systematic definition and specification of color. These schemes vary tremendously in their design principles and, consequently, in the level of accuracy, repeatability and intuitiveness with which they define color sensation. Each is also realized somewhat differently. Some providing purely mathematical three-dimensional descriptions of color while others embody physical samples to illustrate a color as well as its relationship to other colors. Some attempt to equate numerical representation with a perceptual correlate while others have no basis in color perception at all.Such Color Spaces can be considered both language and framework—providing a system of reference and, in some cases, of order where the relationships between colors is easily communicated or perceived and a given color can be defined in relation to all other colors. This paper will provide a brief review of color spaces emphasizing their structure, use, and underscoring their role in the effective transformation and transportability of color.

This paper is about human perception of color and brightness. It is well known that a light of a given spectral energy distribution can produce many alternative percepts depending on other lights nearby or viewed previously. Consider, for example, a patch of light that appears white when viewed against a dim achromatic background. The same patch appears charcoal gray when viewed against an intense background. Varying the background also affects color perception. A patch that appears orange against the dim background is perceived as brown on the intense one. Typically, the influence of background light on color or brightness is inferred from measurements of the change in appearance of one light (a patch) caused by introducing a second light (either a surrounding background or an ‘adapting field’ on which the patch is superimposed). This work has been fruitful but, as discussed below, has important limitations for understanding the color and brightness of visual stimuli composed of more than two lights.

Colorimetric measurements are equally influenced by the reflectance spectrum of the object and the illumination spectrum of the light. The 1931 CIE colorimetric measurements are made one pixel at a time; they integrate the radiances at each wavelength with three colormatching functions so as to generate three Tristimulus Values for one pixel. No information from other pixels in the field of view is used in this calculation.Our everyday experience is that color appearance of objects remain the same, regardless of substantial changes in the spectrum of the illuminant. In other words, everyday experience tells us that an object's reflectance spectrum controls appearance, while its illumination spectrum has little influence.This paper will review the history of different hypotheses explaining human color constancy and describe techniques for measuring color appearances. It will review important experiments that measure color sensations and new techniques using the introduction of a new patch in a display that destroys color matches.Human color vision is a field phenomenon. Humans calculate color sensations by comparing pixels across the entire field of view. Global changes in reflectance or illumination cause small changes in appearance: Local changes in reflectance or illumination cause large changes in sensation. The spatial interaction of all pixels in the field of view controls human color appearance.

Color image enhancement is a technique which makes an image more vivid for human vision. Most affecting color elements for the image are intensity, contrast and saturation. In handling these color elements with the conventional coordinates, their geometric form is important in view of valid gamut. Of them, IHS coordinate appropriately represents human color perception and it is easy to manipulate hue, intensity and saturation of the image. The geometric form of this coordinate is, however, nonlinear, so that it is difficult to control the element values since they may exceed the valid gamut. In this paper, a modified IHS coordinate system is proposed to remedy the nonlinearity of IHS system. The proposed coordinate is derived to linearize the relationship between the saturation and intensity. To improve the image quality, contrast is increased by maximizing the dynamic range of intensity, and saturation is normalized in full range of the intensity because the ratio between the changed saturation values should be maintained the same ratio as between original values. Hue is preserved to keep the characteristic property of the color image. This coordinate system is easy to enhance color images and avoid the gamut-overflow problem.

Printing applications require to convert RGB displayable pictures into four printing process components: yellow, magenta, cyan and black. The printing process generates black color by two mechanisms: substractive mixture of YMC components and by K component. In almost all cases, the RGB picture on the display has different colors to the YMCK converted printed picture. The colors of the YMCK printed picture can be simulated on the RGB display. Simulation is a complex process, which depends on the printer (ink, paper, printing technique, dot gain, UCR or GCR corrections), monitor (RGB phosphor components, gamma correction, brightness and saturation adjustments) as well as observation conditions (illuminant, reflections).In the paper, a neural network is proposed as an alternative solution for RGB-YMCK color conversion, in order to obtain closer color appearance between RGB image and the corresponding YMCK printed image. The YMCK data, as inputs, and the RGB data resulted from simulation of YMCK printed colors, as outputs, are used to learn the neural net-work in order to perform the global color conversion from RGB to YMCK. The general RGB simulation process of the printed YMCK colors is not bidirectional, so that, the network finds one possible transformation with a certain probability, strongly dependent on the learning data which determines the weights of the neural network.

In characterizing a color hardcopy device, it is necessary to establish the relationship between the input signals that drive the device and the colorimetric response of the device to these signals. Most printing devices are not adequately characterized by a simple linear transformation; hence one is left with the choice of either measuring the printer's colorimetric response at numerous points throughout the input signal space, or deriving a model to predict the printer's response. In this paper, we examine a model based approach to device characterization. In particular, we focus on a model developed by Hans Neugebauer for binary color printers employing a rotated halftone dot screen. In their simplest form, the so called “Neugebauer equations” are used to predict the broadband reflectance of a halftone pattern printed on paper. In this paper, we investigate the accuracy of the basic Neugebauer equations and several of its modifications. These equations involve some basic constraints which we will assume to be fulfilled throughout the discussion.An important application of the Neugebauer model is the calibration of binary color printers. Calibration, which requires a mapping from colorimetric signal space to the printer signal space, is the inverse problem of device characterization. Hence, calibration requires that the Neugebauer model be inverted. Since the Neugebauer equations are nonlinear, the inversion is not trivial, and requires numerical or statistical approaches (see [3] for examples). In this paper, we do not deal with the inverse calibration problem; rather, we focus only on the forward characterization problem. We evaluate and compare the use of various Neugebauer models to predict the colorimetric response of a Xerox 5775 color printer. This printer uses xerographic technology with four colorants, and has a resolution of 406 × 1624 dpi.

Transformations of digitized color images in perceptually-uniform CIELUV color space and their perceptual relevance were investigated. Chroma variation was chosen as the first step of a series of investigations into possible transformations (including lightness, hue-angle, chroma, etc.). To obtain the information about the perceptual consequences of the chosen transformations, perceptual image quality, naturalness, and colorfulness were measured by means of scaling. The results suggest that in general a more colorful image than the original one is preferred.

The color reproduction systems, such as a color copier and/or a color printer, are widely used to visualize any graphical information as recent progress in electrophotographic technique. However, some kinds of problems have occurred because the color reproduction theories for these systems are based on the densitometric color value that depend on the difference of physical characteristics of color reproduction systems and/or chemical characteristics of the color materials; the difference of the reproduced color between each color reproduction system. Therefore, it is necessary to adopt CIE L*a*b* value or other colorimetric values as a device-independent representation to reproduce color accurately. Furthermore, we require a method of transformation between device-dependent color representation and independent color representations. Recently, Irie et al. and Funahashi have mathematically proven that the three-layered artificial neural network can approximately realize the continuous mapping with any accuracy unless the number of unit in hidden layer of the network is limited.In this study, we propose the transformation method which realizes the nonlinear mapping from CIE L*a*b* value to CMY dot area size by using a multilayered artificial neural network with a back propagation (BP) learning algorithm. The transformation accuracy of the proposed method was evaluated in terms of the color difference between original color chips and the reproduced color chips which correspond to the output of the trained artificial neural network. We show that the ability of the nonlinear mapping of the neural network can provide a practical and an efficient transformation method for color representation.

Color devices such as scanners, printers and CRTs have device specific coordinate systems. It is desirable to be able to produce mappings between the coordinate system of a particular device and a device independent coordinate system that closely approximates human perception. In this way, color coordinates can easily be specified, transformed, or transported between various input and output devices. Mappings between color coordinate spaces can be achieved by function restoration, when a number of input-output samples of the mapping are available. Feed-forward multi-layer neural network have been shown to be able to perform non-linear non-parametric functional restoration, as is the case with color coordinate mapping. This type of network was used to map the Lab coordinate space onto the RGB coordinate space of an actual and a computer modeled dye sublimation printer. A neuron activation function, is introduced herein, which has advantages that would be useful to function restoration problems such as color mapping. The effectiveness of this model is tested by observing the error between the model's prediction and the ideal correct output on a number of known samples.