Luminance contrast is the basis of text legibility, and maintaining luminance contrast is essential for any color selection algorithm. In principle, it can be calculated precisely on a sufficiently well-calibrated display surface, but calibration is very expensive. Consequently, most current systems deal with contrast using heuristics. However, the usual CRT setup puts the display surface into a state that is relatively predictable. Luminance values can be estimated based on this state, and these luminance values have been used to calculate contrast using the Michelson definition. This paper proposes a method for determining the contrast of colored areas displayed on a CRT. It uses a contrast metric that is in wide use in visual psychophysics and shows that the metric can be approximated reasonably without display measurement, as long as it is possible to assume that the CRT has been adjusted according to usual CRT setup standards.

Background: The gamut of a color CRT is defined by its three primary colors, each produced by a phosphor/electron gun combination. Light from the primaries combines additively, so the color gamut is a subset of a three dimensional vector space [1]. With the primaries as basis vectors normalized to 1.0, the color gamut is a unit cube, known as the RGB color geometry, since the three primaries are usually red, green, and blue. User interaction via RGB is generally thought to be counterintuitive, and transformations of RGB, such as Smith's HSV geometry [10] which is derived from centuries old artists' models [2], are more popular. More recent color theories, based on psychophysical and physiological models of early visual processing, suggest that more intuitive geometries may be possible. The RGYB geometry is based on two recent discoveries about the human visual system. First, the three color signals from the cone receptors are organized into three opponent channels [1, 7]. A single achromatic channel indicates lightness or brightness. Two chromatic channels, red/green and yellow/blue, signal the chromatic quantities. Second, signals on the achromatic channel are easily distinguishable from signals on the chromatic ones [6]. Consequently, it is usual to represent colors as a set of surfaces of colors that vary in chromaticity, each at a different level of brightness. Examples are as diverse as CIE chromaticity coordinates, the CIELUV uniform color space, the Munsell color system, and computer graphics color spaces such as HSV and HLS [10, 12].

Principles and techniques useful for calibrated color reproduction are defined. These results are derived from a project to take digital images designed on a variety of different color monitors and accurately reproduce them in a journal using digital offset printing. Most of the images printed were reproduced without access to the image as viewed in its original form; the color specification was derived entirely from calorimetric specification. The techniques described here are not specific to offset printing and can be applied equally well to other digital color devices. The reproduction system described is calibrated using CIE tristimulus values. An image is represented as a set of three-dimensional points, and the color output device as a three-dimensional solid surrounding the set of all reproducible colors for that device, called its gamut . The shapes of the monitor and the printer gamuts are very different, so it is necessary to transform the image points to fit into the destination gamut, a process we call gamut mapping . This paper describes the principles that control gamut mapping. Included also are some details on monitor and printer calibration, and a brief description of how digital halftone screens for offset printing are prepared.

A two-part experiment was conducted to investigate the effects of aliasing artifacts and screen resolution on a simple visual recognition task. The results indicate that in many cases far less realism may be necessary in synthetic computer-generated imagery than is often assumed in the literature. The first part of the experiment comprised a subjective rating of image quality, the second part measured task effectiveness of image quality. In the second part subjects were asked to discriminate between images of two types of objects built from cubes, similar to objects used in experiments involving mental rotation.

The increasing availability of affordable color raster graphics displays has made it important to develop a better understanding of how color can be used effectively in an interactive environment. Most contemporary graphics displays offer a choice of some 16 million colors; the user's problem is to find the right color. Folklore has it that the RGB color space arising naturally from color display hardware is user-hostile and that other color models such as the HSV scheme are preferable. Until now there has been virtually no experimental evidence addressing this point. We describe a color matching experiment in which subjects used one of two tablet-based input techniques, interfaced through one of five color models, to interactively match target colors displayed on a CRT. The data collected show small but significant differences between models in the ability of subjects to match the five target colors used in this experiment. Subjects using the RGB color model matched quickly but inaccurately compared with those using the other models. The largest speed difference occurred during the early convergence phase of matching. Users of the HSV color model were the slowest in this experiment, both during the convergence phase and in total time to match, but were relatively accurate. There was less variation in performance during the second refinement phase of a match than during the convergence phase. Two-dimensional use of the tablet resulted in faster but less accurate performance than did strictly one-dimensional usage. Significant learning occurred for users of the Opponent, YIQ, LAB, and HSV color models, and not for users of the RGB color model.

The Commission Internationale d'Eclairage system of colorimetry is a method of measuring colours that has been standardized, and is widely used by industries involved with colour. Knowing the CIE coordinates of a colour allows it to be reproduced easily and exactly in many different media. For this reason graphics installations which utilize colour extensively ought to have the capability of knowing the CIE coordinates of displayed colours, and of displaying colours of given CIE coordinates. Such a capability requires a function which transforms video monitor gun voltages (RGB colour space) into CIE coordinates (XYZ colour space), and vice versa. The function incorporates certain monitor parameters. The purpose of this paper is to demonstrate the form that such a function takes, and to show how the necessary monitor parameters can be measured using little more than a simple light meter. Because space is limited, and because each user is likely to implement the calibration differently, few technical details are given, but principles and methods are discussed in sufficient depth to allow the full use of the system. In addition, several visual checks which can be used for quick verification of the integrity of the calibration are described.The paper begins with an overview of the CIE system of colorimetry. It continues with a general discussion of transformations from RGB colour space to XYZ colour space, after which a detailed step-by-step procedure for monitor calibration is presented.