Hue

Last updated August 27, 2024

All colors on this color wheel should appear to have the same lightness and the same saturation, differing only by hue. Colors-i54-ring.png — All colors on this color wheel should appear to have the same lightness and the same saturation, differing only by hue.

In color theory, hue is one of the main properties (called color appearance parameters) of a color, defined technically in the CIECAM02 model as "the degree to which a stimulus can be described as similar to or different from stimuli that are described as red, orange, yellow, green, blue, violet,"^[1] within certain theories of color vision.

Hue can typically be represented quantitatively by a single number, often corresponding to an angular position around a central or neutral point or axis on a color space coordinate diagram (such as a chromaticity diagram) or color wheel, or by its dominant wavelength or by that of its complementary color. The other color appearance parameters are colorfulness, saturation (also known as intensity or chroma),^[2] lightness, and brightness. Usually, colors with the same hue are distinguished with adjectives referring to their lightness or colorfulness - for example: "light blue", "pastel blue", "vivid blue", and "cobalt blue". Exceptions include brown, which is a dark orange.^[3]

In painting, a hue is a pure pigment—one without tint or shade (added white or black pigment, respectively).^[4]

The human brain first processes hues in areas in the extended V4 called globs.^[5]^[6]

Deriving a hue

The concept of a color system with a hue was explored as early as 1830 with Philipp Otto Runge's color sphere. The Munsell color system from the 1930s was a great step forward, as it was realized that perceptual uniformity means the color space can no longer be a sphere.

As a convention, the hue for red is set to 0° for most color spaces with a hue.

**Munsell *hues*; value 6 / chroma 6**
5R \|				5YR \|				5Y \|				5GY \|				5G \|				5BG \|
	201 130 134	201 130 127	201 131 118	200 133 109	197 135 100	193 137 94	187 140 86	181 143 79	173 146 75	167 149 72	160 151 73	151 154 78	141 156 85	127 159 98	115 160 110	101 162 124	92 163 134	87 163 141	82 163 148	78 163 154	73 163 162

5BG \|				5B \|				5PB \|				5P \|				5RP \|				5R \|
	73 163 162	70 162 170	70 161 177	73 160 184	82 158 189	93 156 193	104 154 195	117 151 197	128 149 198	141 145 198	152 142 196	160 140 193	168 138 189	177 135 182	183 134 176	188 132 169	193 131 160	196 130 153	198 130 146	200 130 140	201 130 134

Opponent color spaces

In opponent color spaces in which two of the axes are perceptually orthogonal to lightness, such as the CIE 1976 (L*, a*, b*) (CIELAB) and 1976 (L*, u*, v*) (CIELUV) color spaces, hue may be computed together with chroma by converting these coordinates from rectangular form to polar form. Hue is the angular component of the polar representation, while chroma is the radial component.

Specifically, in CIELAB^[7]

h_{ab}=\mathrm {atan2} (b^{*},a^{*}),

while, analogously, in CIELUV^[7]

h_{uv}=\mathrm {atan2} (v^{*},u^{*})=\mathrm {atan2} (v',u'),

where, atan2 is a two-argument inverse tangent.

Defining hue in terms of RGB

An illustration of the relationship between the "hue" of colors with maximal saturation in HSV and HSL with their corresponding RGB coordinates HSV-RGB-comparison.svg — An illustration of the relationship between the "hue" of colors with maximal saturation in HSV and HSL with their corresponding RGB coordinates

Preucil^[8] describes a color hexagon, similar to a trilinear plot described by Evans, Hanson, and Brewer,^[9] which may be used to compute hue from RGB. To place red at 0°, green at 120°, and blue at 240°,

h_{rgb}=\mathrm {atan2} \left({\sqrt {3}}\cdot (G-B),2\cdot R-G-B\right).

Equivalently, one may solve

\tan(h_{rgb})={\frac {{\sqrt {3}}\cdot (G-B)}{2\cdot R-G-B}}.

Preucil used a polar plot, which he termed a color circle.^[8] Using R, G, and B, one may compute hue angle using the following scheme: determine which of the six possible orderings of R, G, and B prevail, then apply the formula given in the table below.

Ordering	Hue region	$h_{\text{Preucil circle}}$
$R\geq G\geq B$	Orange	$60^{\circ }\cdot {\frac {G-B}{R-B}}$
$G>R\geq B$	Chartreuse	$60^{\circ }\cdot \left(2-{\frac {R-B}{G-B}}\right)$
$G\geq B>R$	Spring Green	$60^{\circ }\cdot \left(2+{\frac {B-R}{G-R}}\right)$
$\ B>G>R\$	Azure	$60^{\circ }\cdot \left(4-{\frac {G-R}{B-R}}\right)$
$B>R\geq G$	Violet	$60^{\circ }\cdot \left(4+{\frac {R-G}{B-G}}\right)$
$R\geq B>G$	Rose	$60^{\circ }\cdot \left(6-{\frac {B-G}{R-G}}\right)$

In each case the formula contains the fraction ${\frac {M-L}{H-L}}$ , where H is the highest of R, G, and B; L is the lowest, and M is the mid one between the other two. This is referred to as the "Preucil hue error" and was used in the computation of mask strength in photomechanical color reproduction.^[10]

Hue angles computed for the Preucil circle agree with the hue angle computed for the Preucil hexagon at integer multiples of 30° (red, yellow, green, cyan, blue, magenta, and the colors midway between contiguous pairs) and differ by approximately 1.2° at odd integer multiples of 15° (based on the circle formula), the maximal divergence between the two.

The process of converting an RGB color into an HSL color space or HSV color space is usually based on a 6-piece piecewise mapping, treating the HSV cone as a hexacone, or the HSL double cone as a double hexacone.^[11] The formulae used are those in the table above.

Additional images for hue in the HSL and HSV systems
Hue in the HSL/HSV encodings of RGB
An image with the hues cyclically shifted in HSL space
The hues in this image of a painted bunting are cyclically rotated over time in HSL.

One might notice that the HSL/HSV hue "circle" do not appear to all be of the same brightness. This is a known issue of this RGB-based derivation of hue.^[12]

Usage in art

Manufacturers of pigments use the word hue, for example, "cadmium yellow (hue)" to indicate that the original pigmentation ingredient, often toxic, has been replaced by safer (or cheaper) alternatives whilst retaining the hue of the original. Replacements are often used for chromium, cadmium and alizarin.

Hue vs. dominant wavelength

Dominant wavelength (or sometimes equivalent wavelength) is a physical analog to the perceptual attribute hue. On a chromaticity diagram, a line is drawn from a white point through the coordinates of the color in question, until it intersects the spectral locus. The wavelength at which the line intersects the spectrum locus is identified as the color's dominant wavelength if the point is on the same side of the white point as the spectral locus, and as the color's complementary wavelength if the point is on the opposite side.^[13]

Hue difference notation

There are two main ways in which hue difference is quantified. The first is the simple difference between the two hue angles. The symbol for this expression of hue difference is $\Delta h_{ab}$ in CIELAB and $\Delta h_{uv}$ in CIELUV. The other is computed as the residual total color difference after Lightness and Chroma differences have been accounted for; its symbol is $\Delta H_{ab}^{*}$ in CIELAB and $\Delta H_{uv}^{*}$ in CIELUV.

Names and other notations

There exists some correspondence, more or less precise, between hue values and color terms (names). One approach in color science is to use traditional color terms but try to give them more precise definitions. See spectral color#Spectral color terms for names of highly saturated colors with the hue from ≈ 0° (red) up to ≈ 275° (violet), and line of purples#Table of highly-saturated purple colors for color terms of the remaining part of the color wheel.

Alternative approach is to use a systematic notation. It can be a standard angle notation for certain color model such as HSL/HSV mentioned above, CIELUV, or CIECAM02. Alphanumeric notations such as of Munsell color system, NCS, and Pantone Matching System are also used.

Related Research Articles

<span class="mw-page-title-main">Munsell color system</span> Color space

In colorimetry, the Munsell color system is a color space that specifies colors based on three properties of color: hue, value (lightness), and chroma. It was created by Albert H. Munsell in the first decade of the 20th century and adopted by the United States Department of Agriculture (USDA) as the official color system for soil research in the 1930s.

HSL and HSV are the two most common cylindrical-coordinate representations of points in an RGB color model. The two representations rearrange the geometry of RGB in an attempt to be more intuitive and perceptually relevant than the cartesian (cube) representation. Developed in the 1970s for computer graphics applications, HSL and HSV are used today in color pickers, in image editing software, and less commonly in image analysis and computer vision.

<span class="mw-page-title-main">Chromaticity</span> Specification of color hue and saturation

Chromaticity is an objective specification of the quality of a color regardless of its luminance. Chromaticity consists of two independent parameters, often specified as hue (h) and colorfulness (s), where the latter is alternatively called saturation, chroma, intensity, or excitation purity. This number of parameters follows from trichromacy of vision of most humans, which is assumed by most models in color science.

The CIELAB color space, also referred to as L*a*b*, is a color space defined by the International Commission on Illumination in 1976. It expresses color as three values: L* for perceptual lightness and a* and b* for the four unique colors of human vision: red, green, blue and yellow. CIELAB was intended as a perceptually uniform space, where a given numerical change corresponds to a similar perceived change in color. While the LAB space is not truly perceptually uniform, it nevertheless is useful in industry for detecting small differences in color.

In color science, the dominant wavelength is a method of aproximating a color's hue. Along with purity, it makes up one half of the Helmholtz coordinates. A color's dominant wavelength is the wavelength of monochromatic spectral light that, if plotted in a chromaticity diagram, the straight line that passes through the color in question and the white point will also pass through this wavelength.

Colorfulness, chroma and saturation are attributes of perceived color relating to chromatic intensity. As defined formally by the International Commission on Illumination (CIE) they respectively describe three different aspects of chromatic intensity, but the terms are often used loosely and interchangeably in contexts where these aspects are not clearly distinguished. The precise meanings of the terms vary by what other functions they are dependent on.

In color science, a color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components. When this model is associated with a precise description of how the components are to be interpreted, taking account of visual perception, the resulting set of colors is called "color space."

TSL color space is a perceptual color space which defines color as tint, saturation, and lightness. Proposed by Jean-Christophe Terrillon and Shigeru Akamatsu, TSL color space was developed primarily for the purpose of face detection.

A color solid is the three-dimensional representation of a color space or model and can be thought as an analog of, for example, the one-dimensional color wheel, which depicts the variable of hue ; or the 2D chromaticity diagram, which depicts the variables of hue and spectral purity. The added spatial dimension allows a color solid to depict the three dimensions of color: lightness, hue, and colorfulness, allowing the solid to depict all conceivable colors in an organized three-dimensional structure.

<span class="mw-page-title-main">Lightness</span> Property of a color

Lightness is a visual perception of the luminance $of an object. It is often judged relative to a similarly lit object. In colorimetry and color appearance models, lightness is a prediction of how an illuminated color will appear to a standard observer. While luminance is a linear measurement of light, lightness is a linear prediction of the human perception of that light.$

In colorimetry, the CIE 1976L*, 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, 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.

In colorimetry, CIECAM02 is the color appearance model published in 2002 by the International Commission on Illumination (CIE) Technical Committee 8-01 and the successor of CIECAM97s.

In color science, color difference or color distance is the separation between two colors. This metric allows quantified examination of a notion that formerly could only be described with adjectives. Quantification of these properties is of great importance to those whose work is color-critical. Common definitions make use of the Euclidean distance in a device-independent color space.

Blend modes in digital image editing and computer graphics are used to determine how two layers are blended with each other. The default blend mode in most applications is simply to obscure the lower layer by covering it with whatever is present in the top layer ; because each pixel has numerical values, there also are many other ways to blend two layers.

A color space is a specific organization of colors. In combination with color profiling supported by various physical devices, it supports reproducible representations of color – whether such representation entails an analog or a digital representation. A color space may be arbitrary, i.e. with physically realized colors assigned to a set of physical color swatches with corresponding assigned color names, or structured with mathematical rigor. A "color space" is a useful conceptual tool for understanding the color capabilities of a particular device or digital file. When trying to reproduce color on another device, color spaces can show whether shadow/highlight detail and color saturation can be retained, and by how much either will be compromised.

A color appearance model (CAM) is a mathematical model that seeks to describe the perceptual aspects of human color vision, i.e. viewing conditions under which the appearance of a color does not tally with the corresponding physical measurement of the stimulus source.

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. 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:

In colorimetry, the HSLuvcolor space is a human-friendly alternative to the HSL color space. It was formerly known as "husl". It is a variation of the CIE LCH(uv) color space, where the C (colorfulness) component is replaced by a "Saturation" (S) component representing the colorfulness percentage relative to the maximum sRGB can provide given the L and H values. The value has nothing to do with "saturation" in color theory.

The Oklab color space is a uniform color space for device independent color designed to improve perceptual uniformity, hue and lightness prediction, color blending, and usability while ensuring numerical stability and ease of implementation. Introduced by Björn Ottosson in December 2020, Oklab and its cylindrical counterpart, Oklch, have been included in the CSS Color Level 4 and Level 5 drafts for device-independent web colors since December 2021. They are supported by recent versions of major web browsers and allow the specification of wide-gamut P3 colors.

References

↑ Mark Fairchild, "Color Appearance Models: CIECAM02 and Beyond". Tutorial slides for IS&T/SID 12th Color Imaging Conference.
↑ "Hue, Value, Saturation | learn". Archived from the original on 2017-06-30. Retrieved October 27, 2017.
↑ C J Bartleson, "Brown". Color Research and Application, 1 : 4, pp. 181–191 (1976).
↑ "The Color Wheel and Color Theory". Creative Curio. 2008-05-16. Archived from the original on 2011-07-05. Retrieved 2011-06-09.
↑ Conway, BR; Moeller, S; Tsao, DY. (2007). "Specialized color modules in macaque extrastriate cortex" (PDF). Neuron. 56 (3): 560–73. doi:10.1016/j.neuron.2007.10.008. PMC 8162777 . PMID 17988638. S2CID 11724926. Archived from the original (PDF) on 2021-03-02. Retrieved 2020-09-10.
↑ Conway, BR; Tsao, DY (2009). "Color-tuned neurons are spatially clustered according to color preference within alert macaque posterior inferior temporal cortex". Proceedings of the National Academy of Sciences of the United States of America. 106 (42): 18034–9. Bibcode:2009PNAS..10618034C. doi: 10.1073/pnas.0810943106 . PMC 2764907 . PMID 19805195.
1 2 Colorimetry, second edition: CIE Publication 15.2. Vienna: Bureau Central of the CIE, 1986.
1 2 Frank Preucil, "Color Hue and Ink Transfer … Their Relation to Perfect Reproduction", TAGA Proceedings, p 102-110 (1953). [TAGA article #T530102, paid registration required from TAGA]
↑ Ralph Merrill Evans, W T Hanson, and W Lyle Brewer, Principles of Color Photography. New York: Wiley, 1953
↑ Miles Southworth, Color Separation Techniques, second edition. Livonia, New York: Graphic Arts Publishing, 1979.
↑ Max K. Agoston (2004). Computer Graphics and Geometric Modelling v. 1: Implementation and Algorithms. Springer. pp. 301–304. ISBN 1-85233-818-0. Archived from the original on 2017-03-21.
↑ Brewer, Cynthia A. (1999). "Color Use Guidelines for Data Representation". Proceedings of the Section on Statistical Graphics. Alexandria, VA: American Statistical Association. pp. 55–60. Archived from the original on 2009-08-07. Retrieved 2024-01-03.
↑ Deane B Judd and Günter Wyszecki, Color in Business, Science, and Industry. New York: Wiley, 1976.

External links

Editing of hue in photography

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[1] Mark Fairchild, "Color Appearance Models: CIECAM02 and Beyond". Tutorial slides for IS&T/SID 12th Color Imaging Conference.

[2] "Hue, Value, Saturation | learn". Archived from the original on 2017-06-30. Retrieved October 27, 2017.

[Bartleson1976-3] C J Bartleson, "Brown". Color Research and Application, 1 : 4, pp. 181–191 (1976).

[4] "The Color Wheel and Color Theory". Creative Curio. 2008-05-16. Archived from the original on 2011-07-05. Retrieved 2011-06-09.

[Conway07-5] Conway, BR; Moeller, S; Tsao, DY. (2007). "Specialized color modules in macaque extrastriate cortex" (PDF). Neuron. 56 (3): 560–73. doi:10.1016/j.neuron.2007.10.008. PMC 8162777 . PMID 17988638. S2CID 11724926. Archived from the original (PDF) on 2021-03-02. Retrieved 2020-09-10.

[Conway-6] Conway, BR; Tsao, DY (2009). "Color-tuned neurons are spatially clustered according to color preference within alert macaque posterior inferior temporal cortex". Proceedings of the National Academy of Sciences of the United States of America. 106 (42): 18034–9. Bibcode:2009PNAS..10618034C. doi: 10.1073/pnas.0810943106 . PMC 2764907 . PMID 19805195.

[CIE1986-7] 1 2 Colorimetry, second edition: CIE Publication 15.2. Vienna: Bureau Central of the CIE, 1986.

[Preucil1953-8] 1 2 Frank Preucil, "Color Hue and Ink Transfer … Their Relation to Perfect Reproduction", TAGA Proceedings, p 102-110 (1953). [TAGA article #T530102, paid registration required from TAGA]

[Evans1953-9] Ralph Merrill Evans, W T Hanson, and W Lyle Brewer, Principles of Color Photography. New York: Wiley, 1953

[Southworth-10] Miles Southworth, Color Separation Techniques, second edition. Livonia, New York: Graphic Arts Publishing, 1979.

[11] Max K. Agoston (2004). Computer Graphics and Geometric Modelling v. 1: Implementation and Algorithms. Springer. pp. 301–304. ISBN 1-85233-818-0. Archived from the original on 2017-03-21.

[12] Brewer, Cynthia A. (1999). "Color Use Guidelines for Data Representation". Proceedings of the Section on Statistical Graphics. Alexandria, VA: American Statistical Association. pp. 55–60. Archived from the original on 2009-08-07. Retrieved 2024-01-03.

[Judd1976-13] Deane B Judd and Günter Wyszecki, Color in Business, Science, and Industry. New York: Wiley, 1976.

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Hue

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