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HCL (Hue-Chroma-Luminance) or LCh refers to any of the many cylindrical color space models that are designed to accord with human perception of color with the three parameters. Lch has been adopted by information visualization practitioners to present data without the bias implicit in using varying saturation.[1][2][3] They are, in general, designed to have characteristics of both cylindrical translations of the RGB color space, such as HSL and HSV, and the L*a*b* color space. Some conflicting definitions of the terms are:

The sRGB gamut plotted within the cylindrical CIELCh color spaces. L is the vertical axis; C is the cylinder radius; h is the angle around the circumference. Left: CIELChab; right: CIELChuv


Color-making attributes

See also: Color vision

HCL concerns the following attributes of color appearance:[A]

The "attribute of a visual sensation according to which an area appears to be similar to one of the perceived colors: red, yellow, green, and blue, or to a combination of two of them".[6]
Lightness, value
The "brightness relative to the brightness of a similarly illuminated white".[6]
Luminance (Y or Lv,Ω)
The radiance weighted by the effect of each wavelength on a typical human observer, measured in SI units in candela per square meter (cd/m2). Often the term luminance is used for the relative luminance, Y/Yn, where Yn is the luminance of the reference white point.
The "attribute of a visual sensation according to which the perceived color of an area appears to be more or less chromatic".[6]

The HSL and HSV color spaces are more intuitive translations of the RGB color space, because they provide a single hue number. However, their luminance variation does not match the way humans perceive color. Perceptually uniform color spaces outperform RGB in cases such as high noise environments.[7]

CIE color spaces

CIE-based LCh color spaces are transformations of the two chroma values (ab or uv) into the polar coordinate. The source color spaces are still very well-regarded for their uniformity, and the transformation does not cause degradation in this aspect. See the respective articles for how the underlying coordinates are derived.

Sarifuddin 2005

Sarifuddin, noting the lack of blue hue consistency of CIELAB—a common complaint among its users—[8] decided to make their own color space by mashing up some of the features.[5]

According to the Stack Overflow user Tatarize, what Sarifuddin proposes as "HCL" is algorithmically similar to HSL. While pointing out advantages in computational efficiency, they argue that Sarifuddin's work does not represent a significant improvement over the CIELAB color space while showing failure to reproduce the paper's claims.[9][10] They also propose what they consider to be an improved version of Sarifuddin's algorithm.[11][original research]

Other color appearance models

In general, any color appearance model with a lightness and two chroma components can also be transformed into a HCL-type color space by turning the chroma components into polar coordinates.


CIELCh has been implemented in a wide range of ways: as programmatic code for generating color swatches in statistics tools, as standalone tools for designing and testing swatches, or as libraries that allow other programs to use the color space. Some implementations include:


  1. ^ "Clearly, if color appearance is to be described in a systematic, mathematical way, definitions of the phenomena being described need to be precise and universally agreed upon."[6]
  1. ^ a b 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.
  2. ^ a b Zeileis, Achim; Hornik, Kurt; Murrell, Paul (2009). "Escaping RGBland: Selecting Colors for Statistical Graphics" (PDF). Computational Statistics & Data Analysis. 53 (9): 3259–3270. doi:10.1016/j.csda.2008.11.033.
  3. ^ 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. Bibcode:2015BAMS...96..203S. doi:10.1175/BAMS-D-13-00155.1. hdl:10419/101098.
  4. ^ a b Zeileis, Achim; Fisher, Jason C.; Hornik, Kurt; Ihaka, Ross; McWhite, Claire D.; Murrell, Paul; Stauffer, Reto; Wilke, Claus O. (2020). "colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes". Journal of Statistical Software. 96 (1): 1–49. arXiv:1903.06490. doi:10.18637/jss.v096.i01. S2CID 80628405.
  5. ^ a b Sarifuddin, M. & Missaoui, Rokia (2005). A New Perceptually Uniform Color Space with Associated Color Similarity Measure for Content-Based Image and Video Retrieval (PDF). Multimedia Information Retrieval Workshop, 28th Annual ACM SIGIR Conference. S2CID 17570716. Archived from the original (PDF) on 2019-02-20.. Abstract/long-form corrected report
  6. ^ a b c d Fairchild (2005), pp. 83–93
  7. ^ Paschos, G. (2001). "Perceptually Uniform Color Spaces for Color Texture Analysis: An Empirical Evaluation". IEEE Transactions on Image Processing. 10 (6): 932–937. Bibcode:2001ITIP...10..932P. doi:10.1109/83.923289.
  8. ^ McLellan, M. R.; Lind, L. R.; Kime, R. W. (1995). "Hue Angle Determinations and Statistical Analysis for Multiquadrant Hunter L,a,b Data". Journal of Food Quality. 18 (3): 235–240. doi:10.1111/j.1745-4557.1995.tb00377.x.
  9. ^ tatarize. "HCL color to RGB and backward". Stack Overflow.
  10. ^ Tatarize (4 September 2012). "HCL: a new Color Space for a pack of lies". Ssnot!. Retrieved 22 May 2019.
  11. ^ "algorithm - HCL color to RGB and backward". Stack Overflow. Retrieved 2020-12-08.
  12. ^ a b "colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes". The Comprehensive R Archive Network. 23 January 2023.
  13. ^ a b "Welcome to python-colorspace's documentation!". Read the Docs.
  14. ^ "HCL Wizard".
  15. ^ "Scientific colour maps". Fabio Crameri.
  16. ^ "ac-colors". GitHub.
  17. ^ "Chroma.js". GitHub.
  18. ^ "colorio". PyPI.