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As color imaging has evolved through the years, our toolset for understanding has similarly evolved. Research in color
difference equations and uniform color spaces spawned tools such as CIELAB, which has had tremendous success over
the years. Research on chromatic adaptation and other appearance phenomena then extended CIELAB to form the basis
of color appearance models, such as CIECAM02. Color difference equations such as CIEDE2000 evolved to reconcile
weaknesses in areas of the CIELAB space. Similarly, models such as S-CIELAB were developed to predict more
spatially complex color difference calculations between images. Research in all of these fields is still going strong and
there seems to be a trend towards unification of some of the tools, such as calculating color differences in a color
appearance space. Along such lines, image appearance models have been developed that attempt to combine all of the
above models and metric into one common framework. The goal is to allow the color imaging research to pick and
choose the appropriate modeling toolset for their needs.
Along these lines, the iCAM image appearance model framework was developed to study a variety of color imaging
problems. These include image difference and image quality evaluations as well gamut mapping and high-dynamic
range (HDR) rendering. It is important to stress that iCAM was not designed to be a complete color imaging solution,
but rather a starting point for unifying models of color appearance, color difference, and spatial vision. As such the
choice of model components is highly dependent on the problem being addressed. For example, with CIELAB it clearly
evident that it is not necessary to use the associated color difference equations to have great success as a deviceindependent
color space. Likewise, it may not be necessary to use the spatial filtering components of an image
appearance model when performing image rendering.
This paper attempts to shed some light on some of the confusions involved with selecting the desired components for
color imaging research. The use of image appearance type models for calculating image differences, like S-CIELAB
and those recommended by CIE TC8-02 will be discussed. Similarly the use of image appearance for HDR applications,
as studied by CIE TC8-08, will also be examined. As with any large project, the easiest way to success is in
understanding and selecting the right tool for the job.
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