The CIECAM02 Color Appearance Model

The CIE Technical Committee 8-01, color appearance models for color management applications, has recently proposed a single set of revisions to the CIECAM97s color appearance model. This new model, called CIECAM02, is based on CIECAM97s but includes many revisions1-4 and some simplifications. A partial list of revisions includes a linear chromatic adaptation transform, a new non-linear response compression function and modifications to the calculations for the perceptual attribute correlates. The format of this paper is an annotated description of the forward equations for the model. Introduction The CIECAM02 color appearance model builds upon the basic structure and form of the CIECAM97s5,6 color appearance model. This document describes the single set of revisions to the CIECAM97s model that make up the CIECAM02 color appearance model. There were many, often conflicting, considerations such as compatibility with CIECAM97s, prediction performance, computational complexity, invertibility and other factors. The format for this paper will differ from previous papers introducing a color appearance model. Often a general description of the model is provided, then discussion about its performance and finally the forward and inverse equations are listed separately in an appendix. Performance of the CIECAM02 model will be described elsewhere7 and for the purposes of brevity this paper will focus on the forward model. Specifically, this paper will attempt to document the decisions that went into the design of CIECAM02. For a complete description of the forward and inverse equations, as well as usage guidelines, interested readers are urged to refer to the TC 8-01 web site8 or to the CIE for the latest draft or final copy of the technical report. This paper is not intended to provide a definitive reference for implementing CIECAM02 but as an introduction to the model and a summary of its structure. Data Sets The CIECAM02 model, like CIECAM97s, is based primarily on a set corresponding colors experiments and a collection of color appearance experiments. The corresponding color data sets9,10 were used for the optimization of the chromatic adaptation transform and the D factor. The LUTCHI color appearance data11,12 was the basis for optimization of the perceptual attribute correlates. Other data sets and spaces were also considered. The NCS system was a reference for the e and hue fitting. The chroma scaling was also compared to the Munsell Book of Color. Finally, the saturation equation was based heavily on recent experimental data.13 Summary of Forward Model A color appearance model14,15 provides a viewing condition specific means for transforming tristimulus values to or from perceptual attribute correlates. The two major pieces of this model are a chromatic adaptation transform and equations for computing correlates of perceptual attributes, such as brightness, lightness, chroma, saturation, colorfulness and hue. The chromatic adaptation transform takes into account changes in the chromaticity of the adopted white point. In addition, the luminance of the adopted white point can influence the degree to which an observer adapts to that white point. The degree of adaptation or D factor is therefore another aspect of the chromatic adaptation transform. Generally, between the chromatic adaptation transform and computing perceptual attributes correlates there is also a non-linear response compression. The chromatic adaptation transform and D factor was derived based on experimental data from corresponding colors data sets. The non-linear response compression was derived based on physiological data and other considerations. The perceptual attribute correlates was derived by comparing predictions to magnitude estimation experiments, such as various phases of the LUTCHI data, and other data sets, such as the Munsell Book of Color. Finally the entire structure of the model is generally constrained to be invertible in closed form and to take into account a sub-set of color appearance phenomena. Viewing Condition Parameters It is convenient to begin by computing viewing condition dependent constants. First the surround is selected and then values for F, c and Nc can be read from Table 1. For intermediate surrounds these values can be linearly interpolated.2 Table. 1. Viewing condition parameters for different surrounds. Surround F c Nc Average 1.0 0.69 1.0 Dim 0.9 0.59 0.95 Dark 0.8 0.525 0.8 The value of FL can be computed using equations 1 and 2, where LA is the luminance of the adapting field in cd/m2. Note that this two piece formula quickly goes to very small values for mesopic and scotopic levels and while it may resemble a cube-root function there are considerable differences between this two-piece function and a cube-root as the luminance of the adapting field gets very small. k =1/ 5L A +1 ( ) (1) ! F L = 0.2k 4 5L A ( ) + 0.1 1" k4 ( ) 2 5L A ( ) 1/ 3 (2) The value n is a function of the luminance factor of the background and provides a very limited model of spatial color appearance. The value of n ranges from 0 for a background luminance factor of zero to 1 for a background luminance factor equal to the luminance factor of the adopted white point. The n value can then be used to compute Nbb, Ncb and z, which are then used during the computation of several of the perceptual attribute correlates. These calculations can be performed once for a given viewing condition.