This paper is an attempt to integrate a wide variety of psychophysical experiments into a computational model to calculate color appearance. Having described the fundamentals of such a model, we turn to applying this model to printing wide dynamic range, real-life scenes and finding the best reproduction of an image with limited printer gamut.

The various ways in which pictures can be made by both subtractive and additive displays are reviewed. Systems using these displays are all limited by errors caused by incorrect camera spectral sensitivities, and by the limited gamuts of reproducible colors. Subtractive systems are further limited by the unwanted absorptions of their colorants. Pictorial images are usually assessed by comparison with memories of familiar objects. Because such objects vary considerably in color appearance, the tolerances in images of pictorial scenes are quite large; however, these variations tend to be smallest for hue and largest for colorfulness, so that hue is the most, and colorfulness the least, important attribute in imaging. Six possible different objectives are described: spectral, colorimetric, exact, equivalent, corresponding, and preferred. Possible reasons for preferring increased contrast in images are discussed.

We can now enjoy excellent color reproduction from color photography, color printing, and color television in our every day life with reasonable prices. This was however not achieved within one day but has a long history of improvements.J C Maxwell, for instance, first realized color photography, in as early as 1861. Though colors were indeed reproduced in his color photography, the color reproduction quality must have been very low.However once a break-through was established, the succeeding improvements have been rather rapid. For instance in color photography, photographic emulsions have been continuously improved in order to achieve high speed with finer grains thus giving less granular color images under usual photographing conditions.As for color sensitivities, the introduction of spectral sensitizers was epoch-making. With regard to color dyes, an application of color development was a key technology towards the modern color photography of high image quality. And again, once these break-throughs were established, their improvements have been carried out vigorously and continuously.It should be noted however that these improvements have not been achieved through systematic developments, but rather through a series of serendipities (discoveries by accidents). Now that the theoretical basis of color reproduction systems has been well established, the color reproduction system may be constructed through a more logical way.At the same time, the rapid growth of modern computer technology has now been favorably employed for improving color image quality. It realizes what would have been impossible in an analog system like conventional color photography through a full exploitation of digital image processing techniques.In the present talk, I will review the process of color reproduction, by taking color photography as an example, in the light of modern colorimetry and will elucidate implicit interrelations between various factors affecting the color reproduction quality.I will also demonstrate some noteworthy improvements when digital color imaging techniques are applied to the color reproduction system. Though I am taking color photography as an example, the present talk is also applicable to a variety of color imaging systems including color hard copy, color printing, or color television.

In this paper, we investigate a multi-exposure multi-illuminant colorimetry system using a Kodak DCS460c digital camera. Our system consists of a measurement device and calibration matrices. The measurement device is formed by a digital camera and a set of filters, and the term multi-exposure refers to the multiple snapshots taken by this camera using different filters. The calibration matrices then take these filtered camera RGB outputs, and return the CIE XYZ tristimulus values under several pre-selected illumination conditions. Our objective is to find the optimal filters and the corresponding calibration matrices that minimize a cost function accounting for errors in L*a*b* space, system robustness, and filter smoothness.We applied this methodology and implemented a two-exposure camera system using Wratten filters. The experimental results are presented in this paper.

An analysis is presented of how the space in which principal component analysis is performed can affect the colorimetric and spectral accuracy of spectral reconstruction. The spectral reconstruction is performed using digital counts given by a new concept of spectral image acquisition constituted by a trichromatic camera combined with absorption filters, instead of the traditional monochrome camera and a set of interference filters. The comparison of the spectral reconstruction performance in each space shows the advantages and disadvantages of using alternative spaces rather than reflectance.

At an early stage in almost all colour reproduction pipelines device RGBs are transformed to CIE XYZs. This transformation is called colour correction. Because the XYZ matching functions are not a linear combination of device spectral sensitivities there are some colours which look the same to a device but have quite different XYZ tristimuli. That such device metamerism exists is well known, yet the problem has not been adequately addressed in the colour correction literature. In this paper, we examine in detail the role that metamers play in developing a new colour correction algorithm.Our approach works in two stages. First, for a given RGB we characterise these to fall possible camera metamers. In the second stage this set is projected onto the XYZ colour matching functions. This results in a set of XYZs any one of which might be the correct answer for colour correction. Good colour correction results by choosing the middle of the set. We call the process of computing the set of metamers, projecting them to XYZs and performing selection, metamer constrained colour correction.Experiments demonstrate that our new method significantly outperforms traditional linear correction methods. For the particular case of saturated colours (these are among the most difficult to deal with) the error is halved on average; the maximum error is reduced by a factor of 4.

For applications where colorimetric information is insufficient to characterize an input scene or document, multispectral image capture (i.e. for more than three records) has been suggested. Experimental cameras have been described, as have the results of signal processing to extract useful spectral and colorimetric information. Previous reports have addressed both system accuracy and precision, the latter as influenced by random pixel-to-pixel image noise. Another contributor to system precision is signal quantization. Statistics are computed for various levels of uniform and non-uniform quantization. The resulting errors in the estimated object spectral reflectance factor and subsequent colorimetric transformation are addressed. The comparison of these errors with those due to stochastic noise sources indicates that both are influenced by the image processing employed.

The spectral sensitivities of image capture devices should be carefully designed to guarantee colorimetric color reproduction. As a standard color test chart, ISO/DIS 12641 is conveniently used for calibrating the scanners or printers. However, its color gamut is not wide enough to evaluate the devices such as digital camera & display and the color distributions are not uniform systems because the chart is a photographic print. This paper presents a virtual color target to estimate the spectral goodness of color devices. A virtual spectrum with given L*a*b* value is generated from the fundamental metamer uniquely obtained by inverse projection of XYZ tristimulus value and an addition of arbitrary metameric black spectrum. The goodness of typical color input sensors is measured and compared with actual color chips using this virtual spectral target.

Knowledge of the full illuminant spectral power distribution is useful for many imaging applications. In most applications, however, accurate estimation is impossible because very few color measurements are made. In many of these cases, however, a great deal is known about the potential set of illuminants. In these cases, classification of scene illumination, rather than estimation of the full spectral power distribution of the illumination, is appropriate and useful. We analyze illuminant classification algorithms designed to group images by illuminant color temperature. To classify the illumination color temperature, a version of the correlation method suggested by Finlayson and colleagues is used. The original algorithm uses chromaticity coordinates, and thus does not use the fact that bright image regions contain more information about the illuminant than dark regions. Using calibrated images with known illuminants, we find that the original correlation method can be improved by using a scaled version of the red and blue sensor responses. When applied to these quantities, the algorithm is more sensitive to differences in illuminant color temperature. Then, we consider an application of the classification algorithm to the problem of rendering a color image acquired under one illumination under a second illuminant, with a different color temperature. This algorithm uses the ratio of R, G, and B sensor responses under different illuminants. The proposed method is applied to an image database of real scene.

Environmental laws mandate the protection of visibility conditions in national parks, and wilderness areas from atmospheric haze due to the emissions of anthropogenic air pollutants. To calculate the improvement in visibility that results from the reduction of these air pollutants, it is necessary to quantify the relationship of haze to the color appearance of objects being viewed through it. To this end, a field study was conducted in the Great Smoky Mountains National Park in eastern Tennessee. Color appearance of objects was quantified by color matching with a visual colorimeter. The Hunt94 color appearance model proved to be an invaluable tool that allowed color appearance of natural targets determined by an observer with a visual colorimeter to be compared to the color appearance predicted from the observed spectra of the target. The variable outside adapting conditions were quantified in terms of the model parameters by finding the values of the parameters that gave the best agreement between observer and spectrophotometer for a set of standard color cards. These model parameters were then applied to the natural targets. In this way, the differences between the adapting and observing conditions of the visual colorimeter and the natural outside environment could be reconciled. The apparent hue, colorfulness, and lightness of objects seen at a distance through haze are strongly dependent on perceived transparency of the atmosphere. The hue is approximately constant with changes in the optical depth of the haze. Lightness behaves similarly to hue. Colorfulness decreases exponentially with optical depth.