In colorimetry, the **CIE 1976** *L**, *u**, *v****color space**, commonly known by its abbreviation **CIELUV**, is a color space adopted by the International Commission on Illumination (CIE) in 1976, as a simple-to-compute transformation of the 1931 CIE XYZ color space, but which attempted perceptual uniformity. It is extensively used for applications such as computer graphics which deal with colored lights. Although additive mixtures of different colored lights will fall on a line in CIELUV's uniform chromaticity diagram (dubbed the *CIE 1976 UCS*), such additive mixtures will not, contrary to popular belief, fall along a line in the CIELUV color space unless the mixtures are constant in lightness.

## Historical background

CIELUV is an Adams chromatic valence color space, and is an update of the CIE 1964 (*U**, *V**, *W**) color space (CIEUVW). The differences include a slightly modified lightness scale, and a modified uniform chromaticity scale in which one of the coordinates, *v*′, is 1.5 times as large as *v* in its 1960 predecessor. CIELUV and CIELAB were adopted simultaneously by the CIE when no clear consensus could be formed behind only one or the other of these two color spaces.

CIELUV uses Judd-type (translational) white point adaptation (in contrast with CIELAB, which uses a "wrong" Kries transform).^{[1]} This can produce useful results when working with a single illuminant, but can predict imaginary colors (i.e., outside the spectral locus) when attempting to use it as a chromatic adaptation transform.^{[2]} The translational adaptation transform used in CIELUV has also been shown to perform poorly in predicting corresponding colors.^{[3]}

## XYZ → CIELUV and CIELUV → XYZ conversions

For typical images, *u** and *v** range ±100 %. By definition, 0 ≤ *L** ≤ 100 %.

### The forward transformation

CIELUV is based on CIEUVW and is another attempt to define an encoding with uniformity in the perceptibility of color differences.^{[4]} The non-linear relations for *L**, *u**, and *v** are given below:^{[4]}

The quantities *u*′_{n} and *v*′_{n} are the (*u*′, *v*′) chromaticity coordinates of a "specified white object" – which may be termed the white point – and *Y*_{n} is its luminance. In reflection mode, this is often (but not always) taken as the (*u*′, *v*′) of the perfect reflecting diffuser under that illuminant. (For example, for the 2° observer and standard illuminant C, *u*′_{n} = 0.2009, *v*′_{n} = 0.4610.) Equations for *u*′ and *v*′ are given below:^{[5]}^{[6]}

### The reverse transformation

The transformation from (*u*′, *v*′) to (*x*, *y*) is:^{[6]}

The transformation from CIELUV to XYZ is performed as follows:^{[6]}

## Cylindrical representation (CIELCH)

The cylindrical version of CIELUV is known as CIE LCh_{uv} (or CIE HLC_{uv}), where *C**_{uv} is the chroma and *h*_{uv} is the hue:^{[6]}

where atan2 function, a "two-argument arctangent", computes the polar angle from a Cartesian coordinate pair.

Furthermore, the saturation correlate can be defined as:

Similar correlates of chroma and hue, but not saturation, exist for CIELAB. See Colorfulness for more discussion on saturation.

CIE Lch(uv), or HCL color space (Hue-Chroma-Luminance) in the information visualization community, is increasingly seen as a way to help with presenting data without the bias implicit in using varying saturation. See the HCL color space article for more information on this topic.^{[7]}^{[8]}^{[9]}

## Color and hue difference

The color difference can be calculated using the Euclidean distance of the (*L**, *u**, *v**) co-ordinates.^{[6]} It follows that a chromaticity distance of corresponds to the same Δ*E**_{uv} as a lightness difference of Δ*L** = 1, in direct analogy to CIEUVW.

The Euclidean metric can also be used in CIELCH, with that component of Δ*E**_{uv} attributable to difference in hue as^{[4]} Δ*H** = √*C**_{1}*C**_{2} 2 sin (Δ*h*/2), where Δ*h* = *h*_{2} – *h*_{1}.

## See also

## References

**^**Judd, Deane B. (January 1940). "Hue saturation and lightness of surface colors with chromatic illumination".*JOSA*.**30**(1): 2–32. doi:10.1364/JOSA.30.000002.**^**Mark D Fairchild,*Color Appearance Models.*Reading, MA: Addison-Wesley, 1998.**^**D. H. Alman, R. S. Berns, G. D. Snyder, and W. A. Larson, "Performance testing of color difference metrics using a color-tolerance dataset."*Color Research and Application,***21**:174-188 (1989).- ^
^{a}^{b}^{c}Schanda, János (2007).*Colorimetry: Understanding the CIE System*. Wiley Interscience. pp. 61–64. ISBN 978-0-470-04904-4.As 24/116 is not a simple ratio, in some publications the 6/29 ratio is used, in others the approximate value of 0.008856 (used in earlier editions of CIE 15). Similarly some authors prefer to use instead of 841/108 the expression (1/3)×(29/6)

^{2}or the approximate value of 7.787, or instead of 16/116 the ratio 4/29. **^***Colorimetry,*second edition: CIE publication 15.2. Vienna: Bureau Central CIE, 1986.- ^
^{a}^{b}^{c}^{d}^{e}Poynton, Charles (2003).*Digital Video and HDTV*. Morgan-Kaufmann. p. 226. ISBN 1-55860-792-7. **^**Ihaka, Ross (2003). "Colour for Presentation Graphics". In Hornik, Kurt; Leisch, Friedrich; Zeileis, Achim (eds.).*Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria*. ISSN 1609-395X.**^**Zeileis, Achim; Hornik, Kurt; Murrell, Paul (2009). "Escaping RGBland: Selecting Colors for Statistical Graphics".*Computational Statistics & Data Analysis*.**53**(9): 3259–3270. doi:10.1016/j.csda.2008.11.033.**^**Stauffer, Reto; Mayr, Georg J.; Dabernig, Markus; Zeileis, Achim (2015). "Somewhere over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations".*Bulletin of the American Meteorological Society*.**96**(2): 203–216. doi:10.1175/BAMS-D-13-00155.1. hdl:10419/101098.

## External links

- Chromaticity diagrams, including the CIE 1931, CIE 1960, CIE 1976
- Colorlab MATLAB toolbox for color science computation and accurate color reproduction (by Jesus Malo and Maria Jose Luque, Universitat de Valencia). It includes CIE standard tristimulus colorimetry and transformations to a number of non-linear color appearance models (CIE Lab, CIE CAM, etc.).