sRGB (standard Red Green Blue) is an RGB color space that HP and Microsoft created The CIE XYZ values must be scaled so that the Y of D65 (“white”) is (X,Y,Z = , , ). This is usually true but some color spaces use. Comparison of some RGB and CMYK colour gamuts on a CIE xy chromaticity diagram. A comparison of the chromaticities enclosed by some color spaces. A color space is a specific organization of colors. In combination with physical device profiling. Color picker, calculator and generator with high precision and contrast test. Converts also RGB, HEX, HSL, HSV/HSB, CMYK and CIE-LAB colors and lots of .

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HSL hue, saturation, lightness and HSV hue, saturation, value are alternative representations of the RGB color modeldesigned in the s by computer graphics researchers to more closely align with the fadbraum human vision perceives color-making attributes.

In these models, colors of each hue are arranged in a radial slice, ce a central axis of ci colors which ranges from black at the bottom to white at the top. The HSV representation models the way paints of different colors mix together, with the saturation dimension resembling various shades of brightly colored paint, and the value dimension resembling the mixture coe those paints with varying amounts of black or white paint.

In each geometry, the central vertical axis comprises the neutralachromaticor gray colors, ranging from black at lightness 0 or value 0, the bottom, to white at lightness 1 or value 1, the top.

In both geometries, the additive primary and secondary colors —red, yellowgreen, cyanblue and magenta —and linear mixtures between adjacent pairs of them, sometimes called pure colorsare arranged around the outside edge of the cylinder with saturation 1.

These saturated colors have lightness 0. Mixing these pure colors with black—producing so-called shades —leaves saturation unchanged. In HSL, saturation is also unchanged by tinting with white, and only farbramu with both black and white—called tones —have saturation less than 1. In HSV, tinting alone reduces saturation.

Confusingly, such diagrams usually label this radial dimension “saturation”, blurring or erasing the distinction between saturation and chroma. Because such an intermediate model—with dimensions hue, chroma, and HSV value or HSL lightness—takes the shape farbrxum a cone or bicone, HSV is often ce the “hexcone model” while HSL is cis called the “bi-hexcone model” fig.

The HSL color space was invented [ further explanation needed ] in by Georges Valensi as a method to add color encoding cue existing monochrome i. Most televisions, computer displays, and projectors produce colors by combining red, green, and blue light in varying intensities—the so-called RGB additive primary colors. The resulting mixtures in RGB color space can reproduce a wide variety of colors called a gamut ; however, the relationship between the constituent amounts of red, green, and blue light and the resulting color is unintuitive, especially for inexperienced users, and for users familiar with subtractive color mixing of paints or traditional artists’ models based on tints and shades fig.

Furthermore, neither additive nor subtractive color models define color relationships the same way the human eye does. For example, imagine we have an RGB display whose color is controlled by three sliders ranging from 0—one controlling the intensity of each of the red, green, and blue primaries.

In the same issue, Joblove and Greenberg [11] described the HSL model—whose dimensions they labeled huerelative chromaand intensity —and compared it to HSV fig. Their model was based more upon how colors are organized and conceptualized in human vision in terms of other color-making attributes, such as hue, lightness, and chroma; as well as upon traditional color mixing methods—e. These models were useful not only because they were more intuitive than raw RGB values, but also because the conversions to and from RGB were extremely fast to compute: Consequently, these models and similar ones have become ubiquitous throughout image editing and graphics software since then.

Some of their uses are described below. Nonetheless, it is worth reviewing those definitions before leaping into the derivation of our models.

Brightness and colorfulness are absolute measures, which usually describe the spectral distribution of light entering the eye, while lightness and chroma are measured relative to some white point, and are thus often used for descriptions of surface colors, remaining roughly constant even as brightness and colorfulness change with different illumination. Saturation can be defined as either the ratio of colorfulness to brightness or that of chroma to lightness. In each of our models, we calculate both hue and what this article will call chromaafter Joblove and Greenbergin the same way—that is, the hue of a color has the same numerical values in all of these models, as does its chroma.

If we take our tilted RGB cube, and project it onto the “chromaticity plane ” perpendicular to the neutral axis, our projection takes the shape of a hexagon, with red, yellow, green, cyan, blue, and magenta at its corners fig. More precisely, both hue and chroma in this model are defined with respect to the hexagonal shape of the projection.


The chroma is the proportion of the distance from the origin to the edge of the hexagon. This ratio is the difference between the largest and smallest values among RGor B in a color. Thus if we add or subtract the same amount from all three of RGand Bwe move vertically within our tilted cube, and do not change the projection.

For points which project onto the origin in the chromaticity plane i. Mathematically, this definition of hue is written piecewise: Sometimes, neutral colors i. These definitions amount to a geometric warping of hexagons into circles: After such a transformation, hue is precisely the angle around the origin and chroma the distance from the origin: Sometimes for image analysis applications, this hexagon-to-circle transformation is skipped, and hue and chroma we’ll denote these H 2 and C 2 are defined by the usual cartesian-to-polar coordinate transformations fig.

The atan2 function, a “two-argument arctangent”, computes the angle from a cartesian coordinate pair. Notice that these two definitions of hue H and H 2 nearly coincide, with a maximum difference between them for any color of about 1.

The two definitions of chroma C and C 2 differ more substantially: While the definition of hue is relatively uncontroversial—it roughly satisfies the criterion that colors of the same perceived hue should have the same numerical hue—the definition of a lightness or value dimension is less obvious: Here are four of the most common fig. All four of these leave the neutral axis alone. The creators of these models considered this a problem for some uses. For example, in a color selection interface with two of the dimensions in a rectangle and the third on a slider, half of that rectangle is made of unused space.

Now imagine we have a slider for lightness: To solve problems such as these, the HSL and HSV models scale the chroma so that it always fits into the range [0, 1] for every combination of hue and lightness or value, calling the new attribute saturation in both cases fig.

To calculate either, simply divide the chroma by the maximum chroma for that value or lightness. The HSI model commonly used for computer vision, which takes H 2 as a hue dimension and the component average I “intensity” as a lightness dimension, does not attempt to “fill” a cylinder by its definition of saturation. Instead of presenting color choice or modification interfaces to end users, the goal of HSI is to facilitate separation of shapes in an image.

Saturation is therefore defined in line with the psychometric definition: See the Use in image analysis section of this article. Using the same name for these three different definitions of saturation leads to some confusion, as the three attributes describe substantially different color relationships; in HSV and HSI, the term roughly matches the psychometric definition, of a chroma of a color relative to its own lightness, but in HSL it does not come close.

Even worse, the word saturation is also often used for one of the measurements we call chroma above C or C 2. The original purpose of HSL and HSV and similar models, and their most common current application, is in color selection tools. At their simplest, some such color pickers provide three sliders, one for each attribute. Most, however, show a two-dimensional slice through the model, along with a slider controlling which particular slice is shown. Several color choosers from the s are shown to the right, most of which have remained nearly unchanged in the intervening time: Some more sophisticated variants are designed for choosing whole sets of colors, basing their suggestions of compatible colors on the HSL or HSV relationships between them.

Most web applications needing color selection also base their tools on HSL or HSV, and pre-packaged open source color choosers exist for most major web front-end frameworks. HSL and HSV are sometimes used to define gradients for data visualizationas in maps or medical images.

Image editing software also commonly includes tools for adjusting colors with reference to HSL or HSV coordinates, or to coordinates in a model based on the “intensity” or luma defined above. In particular, tools with a pair of “hue” and “saturation” sliders are commonplace, dating fsrbraum at least the lates, but various more complicated color tools have also been implemented.

Uniform Color Scale – Wikipedia

For instance, the Unix image viewer and color editor xv allowed six user-definable hue H ranges to be rotated and resized, included a dial -like control for saturation S HSVand a curves -like interface for controlling value V —see fig. Video editors also use these models. These have been copied widely, but several imitators use the HSL e. The applications of such tools include object detection, for instance in robot vision ; object recognitionfor instance of facestextor license plates ; content-based image retrieval ; and analysis of medical images.


For the most part, computer vision algorithms used on color images are straightforward extensions to algorithms designed for grayscale images, for instance k-means or fuzzy clustering of pixel colors, or canny edge detection.

At the simplest, each color component is separately passed through the same algorithm. It is important, therefore, that the features of interest can be distinguished in the color dimensions used.

Because the RGand B components of an object’s color in a digital image are all correlated with the amount of light hitting the object, and therefore with each other, image descriptions in terms of those components make object discrimination difficult.

Starting in the late s, transformations like HSV or HSI were used as a compromise between effectiveness for segmentation and computational complexity. They can be thought of as similar in approach and intent to the neural processing used by human color vision, without agreeing in particulars: John Kender’s master’s thesis proposed the HSI model. In recent years, such models have continued to see wide use, as their performance compares favorably with more complex models, and their computational simplicity remains compelling.

While HSL, HSV, and related spaces serve well enough to, for instance, choose a single color, they ignore much of the complexity of color appearance. Essentially, they trade off perceptual relevance for computation speed, from a time in computing history high-end s graphics workstations, or mids consumer desktops when more sophisticated models would have been too computationally expensive.

If we plot the RGB gamut in a more perceptually-uniform space, such as CIELAB see belowit becomes immediately clear that the red, green, and blue primaries do not have the same lightness or chroma, or evenly spaced hues.

Furthermore, different RGB displays use different primaries, and so have different gamuts. If we take an image and extract the hue, saturation, and lightness or value components, and then compare these to the components of the same name as defined by color scientists, we can quickly see the difference, perceptually.

For example, examine the following images of a fire breather fig.

The original is in the sRGB colorspace. Though none of the dimensions in these spaces match their perceptual analogs, the value of HSV and the saturation of HSL are particular offenders. Such perversities led Cynthia Brewer, expert in color scheme choices for maps and information displays, to tell the American Statistical Association:. Computer science offers a few poorer cousins to these perceptual spaces that may also turn up in your software interface, such as HSV and HLS.

But take a close look; don’t be fooled. farbgaum

Perceptual color dimensions are poorly scaled by the color specifications that are provided in these and some other systems. For example, saturation and lightness are confounded, so a saturation scale may also contain a wide range of lightnesses for example, it may progress from white to green which is a combination of both lightness and saturation.

Likewise, hue and lightness are confounded so, for example, a farbraym yellow and saturated blue may be designated as the same ‘lightness’ but have wide differences in perceived lightness. These flaws make the systems difficult to use to control the look of a farbrahm scheme in a systematic manner.

CIE 1931 color space

If much tweaking is required to achieve the desired effect, the system offers little benefit over grappling with raw specifications in RGB or CMY. If these problems make HSL and HSV problematic for choosing colors or color schemes, they make them much worse for image adjustment. HSL and HSV, as Brewer mentioned, confound perceptual color-making attributes, so that changing any dimension results in non-uniform changes to all three perceptual dimensions, and distorts all of the color relationships in the image.

In the example below fig. Notice how the hue-shifted middle version without such a correction dramatically changes the perceived lightness relationships between colors in the image.

In particular, the turtle’s shell is much darker and has less contrast, and the background water is much lighter. The creators of HSL and HSV were far from the first to imagine colors fitting into conic or spherical shapes, with neutrals running from black to white in a central axis, and hues corresponding to angles around that axis.

Similar arrangements date back to the 18th century, and continue to be developed in the most modern and scientific models. First, we compute chroma, by multiplying saturation by the maximum chroma for a given lightness or value. Next, we find the point on one of the bottom three faces of the RGB cube which has the same hue and chroma as our color and therefore projects onto the same point in the chromaticity plane.