# CIECAM02

**DOI:**https://doi.org/10.1007/978-1-4419-8071-7_6

## Synonyms

## Definition

CIECAM02 is a color appearance model that provides a viewing condition specific method for transforming between tristimulus values and perceptual attribute correlates. This model was first published [1] in 2002 by Division 8 of the International Commission on Illumination (CIE). CIECAM02 was developed for use in color management systems and was based on the previously published CIECAM97s color appearance model [2, 3]. The model provides a number of parameters for defining a viewing condition and also inverse equations for transforming perceptual attribute correlates back to tristimulus values for a given set of viewing conditions. In this way, CIECAM02 can be used to transform perceptual attribute correlates, such as lightness, chroma, and hue, across different viewing conditions.

## Overview

A color appearance model [4, 5] transforms between colorimetry, which specifies if stimuli match, and perceptual attribute correlates, which are scales of lightness, chroma, and hue. To do so, CIECAM02 provides a set of viewing condition parameters in order to model specific color appearance phenomena, such as chromatic adaptation and simultaneous contrast. The resulting perceptual attribute correlates can then be used in research and engineering applications requiring a viewing condition independent color representation, such as color calibration of color printers.

The luminance of the adapting field can be measured directly with an illuminance meter. To incorporate a gray-world assumption and convert to luminance, this is then divided by 5π. The surround setting for the model is categorical and follows roughly the specific application. Dark surrounds are those with no ambient illumination or viewing film projected in a darkened room. Dim surrounds are those in which the ambient illumination is not zero but is also less than 20 % of the scene, print, or display white point, such as home viewing of television with low light levels. Average surround is ambient illumination greater than 20 % of the scene, print, or display white point, such as viewing of surface colors in a light booth. The CIECAM02 model has a set of constants associated with each surround.

Finally the model has an associated white for all calculations. The selection of a white point is a subtle topic and the model suggests two approaches to this issue. First is to use an adopted white point or a computational white point for all calculations. An adopted white point is a fixed value, such as one based on a standard viewing condition or to ensure a specific final mapping for the white point. Second is an adapted white or the white point adapted by the human visual system for a given set of viewing conditions. An adapted white point is one which attempts to as closely as possible match the state of adaptation for a human observer. Note that adapted white points may require experimentation to infer their value, such as for a novel set of viewing conditions or situations with multiple illuminants. In cases where it is not possible to determine the adapted white point, use of an adopted or assumed white point can be convenient.

*D*is shown in Eq. 2. The variable

*L*

_{A}is the luminance of the adapting field and the value of

*F*is a parameter that is computed from the surround setting.

^{2}are shown in Fig. 4. Essentially the surround limits the degree of adaptation and increases with larger values of

*L*

_{A}. Complete adaptation is only achieved with the average surround with high

*L*

_{A}values.

*R*

_{w},

*G*

_{w}, and

*B*

_{w}.

*R*

_{c},

*G*

_{c}, and

*B*

_{c}above to the Hunt-Pointer-Estevez space. This can be done using a 3 by 3 matrix shown in Eq. 6:

*R*′,

*G*′, and

*B*′ values are the HPE values as computed with Eq. 6, and the

*F*

_{L}value is a model parameter that is dependent on the viewing conditions.

*a*and

*b*, are computed according to Eqs. 10 and 11. It should be emphasized that these values of

*a*and

*b*are preliminary and should not be used directly. The value of

*h*or hue is computed using the arctangent of

*b*divided by

*a*. A table of constants is used to compute

*H*or hue quadrature. The resulting

*H*values for red, yellow, green, and blue are 100, 200, 300, and 400, respectively.

*A*is computed according to Eq. 12. The

*R*′

_{a},

*G*′

_{a}, and

*B*′

_{a}values are the nonlinearly compressed values from Eqs. 7 through 9. Next the computation of

*J*or lightness is shown in Eq. 13, while the computation of

*Q*or brightness is shown in Eq. 14. The

*c*and

*z*values are additional model parameters as computed based on the viewing conditions.

*t*is computed using Eq. 15. Next

*C*or chroma is calculated using Eq. 16. Colorfulness or

*M*and saturation

*s*can then be computed using Eqs. 17 and 18.

*a** and

*b** values for CIELAB [7]. Instead a set of correlates such as lightness, chroma, and hue must first be computed and used as polar coordinates. The rectangular coordinates can be computed using Eqs. 19 and 20. Similar coordinates can be calculated for lightness and saturation, with subscript s, and brightness and colorfulness, with subscript M.

It is useful to further compare and contrast CIELAB and CIECAM02. CIELAB has as input the stimulus XYZ values and the white point XYZ values. CIECAM02 has as input the stimulus and white XYZ values and also luminance of the adapting field, the luminance of the background, and the surround setting. CIELAB has a chromatic adaptation transform that consists of a complete von Kries transform in XYZ space, while CIECAM02 has a complete or incomplete von Kries transform in CAT02 RGB space. CIELAB has a cube-root nonlinearity, while CIECAM02 uses a modified hyperbolic function as the nonlinearity. CIELAB uses XYZ data to compute the opponent signal, while CIECAM02 is based on a Hunt-Pointer-Estevez space. Finally CIELAB can be used to compute lightness, chroma, and hue correlates, while CIECAM02 can be used to compute these values as well as brightness, colorfulness, saturation, and hue quadrature values. However, CIELAB has benefited from the additional research in advanced color difference equations and as a result has advanced color difference metrics such as ΔE94 and ΔE 2000 which CIECAM02 does not have. There are encouraging results [8] though for using CIECAM02-based color difference equations.

## Future Directions

CIECAM02 has been a useful and valuable addition to color appearance modeling research. It has provided a single reference point for ongoing research in the area of color appearance modeling. However, a number of researchers have pointed to specific aspects of the complexity that are problematic in some cases. For example, for the darkest colors, it may not be possible to invert the calculations for highly saturated inputs. These values may be outside the spectral locus, but for color management applications that use a fixed intermediate grid of coordinates, this is a shortcoming. Therefore, it seems likely that future work will continue in the area of color appearance modeling, with a future focus on robustness [8] and perhaps simplicity. In spite of these limitations, there is already work integrating CIECAM02 with color management systems, such as the International Color Consortium (ICC) [10, 11]. There has also been work [12] to consider how the model could be further extended to encompass a wider range of viewing conditions, such as mesopic illumination levels. Finally, there is also research [13] in the area of how the model could be used with complex stimuli to create an image appearance model.

## Cross-References

### References

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