iCAM: An Image Appearance Model

Meet iCAM

Simultaneous Contrast and iCAM Lightness Prediction (Right)

Chroma Crispening and iCAM Chroma Prediction (Right)

Spreading and iCAM Hue Prediction (Right)

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. We have introduced 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.

M.D. Fairchild and G.M. Johnson, “The iCAM framework for image appearance, image differences, and image quality,” Journal of Electronic Imaging, in press (2004). Download

G.M. Johnson and M.D. Fairchild, “Rendering HDR images,” IS&T/SID 11th Color Imaging Conference, Scottsdale, 36-41 (2003). Download

G.M. Johnson and M.D. Fairchild, “Measuring images: Differences, Quality, and Appearance,” SPIE/IS&T Electronic Imaging Conference, SPIE Vol. 5007, Santa Clara, 51-60 (2003). Download

M.D. Fairchild and G.M. Johnson, “Image appearance modeling,” SPIE/IS&T Electronic Imaging Conference, SPIE Vol. 5007, Santa Clara, 149-160 (2003). Download

M.D. Fairchild and G.M. Johnson, "Meet iCAM: An Image Color Appearance Model" IS&T/SID 10th Color Imaging Conference, Scottsdale, (2002). Download

iCAM is not considered a finished product, but it is rather a framework designed to focus research on image appearance. We anticipate the evolution of iCAM into a more complete model, and are looking forward to other researchers contributions.

We anticipate releasing the source code to the initial implementation in many different programming languages, including Matlab, IDL, and Mathematica. Right now, we are releasing several Mathematica notebooks that illustrate the equations and parameter used in the CIC publication above. These notebooks can be read with the free Mathreader software, available from Wolfram Software.

There is code a plenty for Matlab and IDL, mostly for the HDR tone mapping. Please goto that page for additional source.

Below is the source and executable iCAM implementation for Microsoft Visual C, kindly donated to the site from Kim Jin-Seo. I have not personally run the code, mostly because I don't generally touch Windows with a long stick...but I certainly appreciate the code.

High Dynamic Range Tone Mapping

Much of our focus thus far has been on using iCAM for high dynamic range tone mapping. This seems to be a pretty popular topic, so it deserves its own special page. Below are some example rendered images from Debevec. There are more examples on the hdr page along with source code aplenty.

Any questions and comments about the source code should be addressed to Garrett M. Johnson