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.