Imaging systems are compared to the human eye in terms of acquisition, spectral sensitivity, transmission, and display. Although the performance of imaging systems is generally of a high standard, there is still room for improvement. At the acquisition stage, the use of flash, or infra-red lighting, results in images with some unnatural features, and there is a general absence of systems that give monochrome images at very low light levels, as are provided by the rod system in the eye. The overlapping nature of the spectral sensitivities of the cones results in unwanted cone stimulations which reduce reproduction gamuts; in printing, extra colorants, such as orange, green, and violet, can be used to extend the gamut and reduce metamerism. Commercially available imaging systems incorporate spectral sensitivities that do not usually exactly match a set of color matching functions, which is a requirement for special applications where high colour accuracy is important. The transmission of image signals in broadcast television makes use of the important luminance/chrominance principle, but full benefit is not achieved because of gamma correction, and only one system makes use of the reduced resolution of the yellowness-blueness channel of the eye. Successful bit reduction in digital images is achieved by taking advantage of the reduced contrast sensitivity of the eye at high spatial frequencies and other effects, but a reduction in the consequent artefacts is desirable. In the visual system, the display is in the cortex, which has an enormous ability to interpret the retinal signals so as to recognise objects, including very efficient compensation for changes in illumination level and color; some improvements in the similar compensation provided in imaging systems are desirable, together with increases in the dynamic ranges available, especially in display devices. Automatic image-enhancement adjustments can be included when making images, but there is room for more sophisticated techniques that avoid impairing some types of scene. A low-cost image-display device for the mass market that is more convenient than the cathode-ray tube remains an important challenge.

Lippmann color photography is the only process known to record physically the spectral composition of any light distribution. It can be regarded as a precursor of Fourier spectroscopy or simply as a physical process for the memorization of colors. The method is described and revisited to explore its scientific impact, and the technical challenges that infer with its realization. The implication of its cultural vigor and the formidable longevity of Lippmann photographs are also described.

Color Assimilation is a very obvious effect, in which two identical stimuli appear very different hues. This paper studies the hypothesis that the broad spectral sensitivity functions of human vision are the cause of these spatial effects. The results show that the long-wave response to middle-wave light, and the middle-wave response to long-wave light introduce spatial effects consistent with observed colors.

A new CIE color appearance model (CIECAM02) has been developed. This paper describes the three major drawbacks of the earlier CIECAM97s model, and shows how the new model performs in these color regions. In addition, both models were tested using available data groups. The results are consistent in that CIECAM02 performed as well as, or better than, CIECAM97s in almost all cases, there being a large improvement in the prediction of saturation. The CIECAM02 model can therefore be considered as a possible replacement for CIECAM97s for all image applications.

For over 20 years, color appearance models have evolved to the point of international standardization. These models are capable of predicting the appearance of spatially-simple color stimuli under a wide variety viewing conditions and have been applied to images by treating each pixel as an independent stimulus. It has been more recently recognized that revolutionary advances in color appearance modeling would require more rigorous treatment of spatial (and perhaps temporal) appearance phenomena. In addition, color appearance models are often more complex than warranted by the available visual data and limitations in the accuracy and precision of practical viewing conditions. Lastly, issues of color difference measurement are typically treated separate from color appearance. Thus, the stage has been set for a new generation of color appearance models. This paper presents one such model called iCAM, for image color appearance model. The objectives in formulating iCAM were to simultaneously provide traditional color appearance capabilities, spatial vision attributes, and color difference metrics, in a model simple enough for practical applications. The framework and initial implementation of the model are presented along with examples that illustrate its performance for chromatic adaptation, appearance scales, color difference, crispening, spreading, high-dynamic-range tone mapping, and image quality measurement. It is expected that the implementation of this model framework will be refined in the coming years as new data become available.

The First Color Imaging Conference in 1993 was organized around the theme of “Transforms and Transportability” and included an invited paper entitled “A Short History of Device Independent Color.” Several developments since then have affected the idea of device independent color and its application to color imaging. These developments include the ICC, which was founded in the same year that this conference series started; the formation of CIE Division 8; the introduction of sRGB and other RGB color spaces; the standardization of color appearance models; the growing use of spectral reproduction techniques; the development of the image state concept; and the phenomenal growth of the web. This paper will update the history of device independent color, describing how device independent color and color interchange and reproduction have been influenced by these developments and what we've heard at past Color Imaging Conferences.

The work of a CIE Division 8 may be compared to the labors of an alchemist. Alchemists tried to combine the four elements: earth, air, fire, and water to create the Philosopher's Stone, which was a substance that would bring wealth, health, and power. Division 8 is struggling to combine color practice, color science, color engineering, and color standards. The progress of each of the six technical committees (TCs) in CIE Division 8 is presented. Four of the five original TCs will present technical reports by 2003. Two of the TCs will then be closed. The TCs have succeeded by finding the appropriate blend of these four elements.

Different large magnitude colour-difference (LCD) data sets were accumulated. A uniform colour space based upon modification of CIELAB was developed to fit each data set. These spaces were then compared to reveal the differences between the different data sets. The results showed that all LCD data sets have fairly similar characteristics to each other except for the Munsell data due to the incorrect balance between the lightness and chromatic data within the latter data set. The present results show that a one unit of Munsell Value appears larger than one unit of Munsell Chroma by a factor of 1.25.

A method for predicting statistics of colour differences computed using a ΔE metric given a value of a different ΔE metric is introduced in this paper. The result of this method is the ability to determine what range of values of one metric can be expected for a given ΔE value computed in terms of another metric. This in turn allows for inter–comparison of results from different studies even if these reported their findings in terms of various colour difference formulae. This paper also illustrates the use of the inter–comparison method proposed in it and shows that the method works with very high levels of accuracy and that it can be applied to a range of colour difference metrics including even Euclidean distance in device–dependent colour spaces. The effect of applying advanced colour difference formula in CAM97s2 space is also illustrated and a close relationship with their original CIELAB–based versions is shown.