Adaptation can be considered as a dynamic mechanism of the human visual system to optimize the visual response to a particular viewing condition. Dark and light adaptation are the changes in visual sensitivity when the level of illumination is decreased or increased, respectively. Chromatic adaptation is the ability of the human visual system to discount the color of the illumination and to approximately preserve the appearance of an object. Chromatic adaptation can be observed by examining a white object under different types of illumination, such as daylight and incandescent. Daylight is "bluer": it contains far more short-wavelength energy than incandescent. However, the white object retains its white appearance under both light sources, as long as the viewer is adapted to the light source.
Image capturing systems, such as scanners and digital cameras, do not have the ability to adapt to an illumination source. Scanners usually have fluorescent light sources. Illumination sources captured by digital cameras vary according to the scene, and often within the scene. Additionally, images captured with these devices are viewed using a wide variety of light sources. The white point of monitors can vary widely, where as hardcopy output is usually evaluated using standard daylight (illuminant D50) simulators. To faithfully reproduce the appearance of image colors, it follows that all image processing systems need to apply a transform that converts the input colors captured under the input illuminant to the corresponding output colors under the output illuminant.
|
|
| Figure 1: To have the same image appearance, the image colors under one viewing illuminant have to be converted to corresponding colors under a second viewing illuminant. This is accomplished with a chromatic adaptation transform. |
Such transformations are called Chromatic Adaptation Transforms (CATs). There has been a significant amount of research in determining CATs that are able to accurately predict color appearance across a change in illumination. Many chromatic adaptation transforms described in the literature are based on a modified form of the von Kries chromatic adaptation model, which states that chromatic adaptation is an independent gain regulation of the three sensors in the human visual system. The recommended transforms currently in use are based on minimizing perceptual error of experimental corresponding color data sets.
In our initial work [1,2] on this subject we have shown that a chromatic adaptation transform derived through spectral sharpening performs as well as the most popular CAT, the linear Bradford transform, and better than most other transforms [3]. Further, we have also shown using a spherical sampling technique that there is a large number of chromatic adaptation transforms that perform equally well as the existing CATs [4]. We have therefore investigated alternative criteria on which to derive a chromatic adaptation transform. For example, we have investigated gamut properties [5] and hue constancy [6] to investigate if such a transform could result in primaries on which to base a white-point independent RGB encoding. We have also looked at the performance of CATs derived on the premise that we would like to maintain the stability of all color ratio pairs under different illumination conditions [7]. This work is ongoing and our aim is to develop a deeper understanding of how our vision system is operating with regard to chromatic adaptation.