Garrett M. Johnson

Matching image color from different cameras

Can images from professional digital SLR cameras be made equivalent in color using simple colorimetric characterization? Two cameras were characterized, these characterizations were implemented on a variety of images, and the results were evaluated both colorimetrically and psychophysically. A Nikon D2x and a Canon 5D were used. The colorimetric analyses indicated that accurate reproductions were obtained. The median CIELAB color differences between the measured ColorChecker SG and the reproduced image were 4.0 and 6.1 for the Canon (chart and spectral respectively) and 5.9 and 6.9 for the Nikon. The median differences between cameras were 2.8 and 3.4 for the chart and spectral characterizations, near the expected threshold for reliable image difference perception. Eight scenes were evaluated psychophysically in three forced-choice experiments in which a reference image from one of the cameras was shown to observers in comparison with a pair of images, one from each camera. The three experiments were (1) a comparison of the two cameras with the chart-based characterizations, (2) a comparison with the spectral characterizations, and (3) a comparison of chart vs. spectral characterization within and across cameras. The results for the three experiments are 64%, 64%, and 55% correct respectively. Careful and simple colorimetric characterization of digital SLR cameras can result in visually equivalent color reproduction.

Appearance can be deceiving: using appearance models in color imaging

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.