Product Code Database
(CRT) gamut'''
The grayed-out horseshoe shape is the entire range of possible chromaticity, displayed in the CIE 1931 chromaticity diagram format (see below). The colored triangle is the gamut available to the sRGB color space typically used in computer monitors; it does not cover the entire space. The corners of the triangle are the for this gamut; in the case of a CRT, they depend on the colors of the phosphors of the monitor. At each point, the brightest possible RGB color of that chromaticity is shown, resulting in the bright Mach band stripes corresponding to the edges of the RGB color cube.]]
In color reproduction and colorimetry, a gamut, or color gamut , is a convex set containing the that can be accurately represented, i.e. reproduced by an output device (e.g. printer or display) or measured by an input device (e.g. camera or visual system). Devices with a larger gamut can represent more colors. Similarly, gamut may also refer to the colors within a defined color space, which is not linked to a specific device. A trichromatic gamut is often visualized as a color triangle. A less common usage defines gamut as the subset of colors contained within an image, scene or video.
The gamut of a device or process is that portion of the color space that can be represented, or reproduced. Generally, the color gamut is specified in the hue–saturation Euclidean plane, as a system can usually produce colors over a wide intensity range within its color gamut. Device gamuts must use real primaries (those that can be represented by a physical spectral power distribution) and therefore are always incomplete (smaller than the human visible gamut). No gamut defined by a finite number of primary color can represent the entire human visible gamut. Three primaries are necessary for representing an approximation of the human visible gamut. More primaries can be used to increase the size of the gamut. For example, while painting with red, yellow and blue pigments is sufficient for modeling color vision, adding further pigments (e.g. orange or green) can increase the size of the gamut, allowing the reproduction of more saturated colors.
While processing a digital image, the most convenient color model used is the RGB model. Printing the image requires transforming the image from the original RGB color model to the printer's CMYK color model. During this process, the colors from the RGB model which are out of gamut must be somehow converted to approximate values within the CMYK model. Simply trimming only the colors which are out of gamut to the closest colors in the destination space would burn the image. There are several algorithms approximating this transformation, but none of them can be truly perfect, since those colors are simply out of the target device's capabilities. This is why identifying the colors in an image that are out of gamut in the target color space as soon as possible during processing is critical for the quality of the final product. It is also important to remember that there are colors inside the CMYK gamut that are outside the most commonly used RGB color spaces, such as sRGB and Adobe RGB.
| + | center]] | Gamuts are commonly represented as areas within the CIE 1931 chromaticity diagram. This ignores the intensity/brightness dimension of the gamut, which is not depicted. Gamuts defined by three primaries are visualized as . |
Gamuts may also be represented in 3D space as a color solid, which includes a visualization of the dynamic range of the device. | ||
The pictures show the gamuts of the sRGB color space (left), which is approximately the one that most computer monitors and Television set have; and the theoretical gamut of surfaces (optimal color solid, or Rösch-MacAdam color solid) (under D65 illumination) (right). The left diagram shows that the shape of the RGB gamut is a triangle between red, green, and blue at lower luminosities; a triangle between cyan, magenta, and yellow at higher luminosities, and a single white point at maximum luminosity. The exact positions of the apexes depends on the emission spectra of the in the display, and on the ratio between the maximum luminosities of the three phosphors (i.e., the color balance or white point). |
*(with classical reflection. Phenomena like fluorescence or structural coloration may cause the color of objects to lie outside the optimal color solid)
The Reflectance of a color is the amount of light of each wavelength that it reflects, in proportion to a given maximum, which has the value of 1 (100%). If the reflectance spectrum of a color is 0 (0%) or 1 (100%) across the entire visible spectrum, and it has no more than two transitions between 0 and 1, or 1 and 0, then it is an optimal color. With the current state of technology, we are unable to produce any material or pigment with these properties.
Thus four types of "optimal color" spectra are possible:
The first type produces colors that are similar to the spectral colors and follow roughly the horseshoe-shaped portion of the CIE xy chromaticity diagram (the spectral locus), but are, in surfaces, more colorfulness, although less Visible spectrum pure. The second type produces colors that are similar to (but, in surfaces, more chromatic and less spectrally pure than) the colors on the straight line in the CIE xy chromaticity diagram (the line of purples), leading to magenta or purple-like colors.
In optimal color solids, the colors of the visible spectrum are theoretically black, because their reflectance spectrum is 1 (100%) in only one wavelength, and 0 in all of the other infinite visible wavelengths that there are, meaning that they have a lightness of 0 with respect to white, and will also have 0 chroma, but, of course, 100% of spectral purity. In short: In optimal color solids, spectral colors are equivalent to black (0 lightness, 0 chroma), but have full spectral purity (they are located in the horseshoe-shaped spectral locus of the chromaticiy diagram).
In linear color spaces, such as LMS or CIE 1931 XYZ, the set of rays that start at the origin (black, (0, 0, 0)) and pass through all the points that represent the colors of the visible spectrum, and the portion of a plane that passes through the violet half-line and the red half-line (both ends of the visible spectrum), generate the "spectrum cone". The black point (coordinates (0, 0, 0)) of the optimal color solid (and only the black point) is tangent to the "spectrum cone", and the white point ((1, 1, 1)) (only the white point) is tangent to the "inverted spectrum cone", with the "inverted spectrum cone" being Symmetry to the "spectrum cone" with respect to the middle gray point ((0.5, 0.5, 0.5)). This means that, in linear color spaces, the optimal color solid is centrally symmetric.
In most color spaces, the surface of the optimal color solid is smooth, except for two points (black and white); and two sharp edges: the "Heat" edge, which goes from black, to red, to orange, to yellow, to white; and the "cool" edge, which goes from black, to deep violet, to blue, to cyan, to white. This is due to the following: If the portion of the reflectance spectrum of a color is spectral red (which is located at one end of the spectrum), it will be seen as black. If the size of the portion of total or reflectance is increased, now covering from the red end of the spectrum to the yellow wavelengths, it will be seen as red. If the portion is expanded even more, covering the green wavelengths, it will be seen as orange or yellow. If it is expanded even more, it will cover more wavelengths than the yellow semichrome does, approaching white, until it is reached when the full spectrum is reflected. The described process is called "cumulation". Cumulation can be started at either end of the visible spectrum (we just described cumulation starting from the red end of the spectrum, generating the "warm" sharp edge), cumulation starting at the violet end of the spectrum will generate the "cool" sharp edge.
If B is the complementary wavelength of wavelength A, then the straight line that connects A and B passes through the achromatic axis in a linear color space, such as LMS or CIE 1931 XYZ. If the reflectance spectrum of a color is 1 (100%) for all the wavelengths between A and B, and 0 for all the wavelengths of the other half of the color space, then that color is a maximum chroma color, semichrome, or full color (this is the explanation to why they were called semichromes). Thus, maximum chroma colors are a type of optimal color.
As explained, full colors are far from being monochromatic. If the spectral purity of a maximum chroma color is increased, its colorfulness decreases, because it will approach the visible spectrum, ergo, it will approach black.
In perceptually uniform color spaces, the lightness of the full colors varies from around 30% in the violetish blue hues, to around 90% in the hues. The chroma of each maximum chroma point also varies depending on the hue; in optimal color solids plotted in perceptually uniform color spaces, semichromes like red, green, blue, violet, and magenta have a high chroma, while semichromes like yellow, orange, and cyan have a slightly lower chroma.
The idea of optimal colors was introduced by the Baltic German chemist Wilhelm Ostwald. Erwin Schrödinger showed in his 1919 article Theorie der Pigmente von größter Leuchtkraft (Theory of Pigments with Highest Luminosity) that the most-saturated colors that can be created with a given total reflectivity are generated by surfaces having either zero or full reflectance at any given wavelength, and the reflectivity spectrum must have at most two transitions between zero and full.
Schrödinger's work was further developed by David MacAdam and . MacAdam was the first person to calculate precise coordinates of selected points on the boundary of the optimal color solid in the CIE 1931 color space for lightness levels from Y = 10 to 95 in steps of 10 units. This enabled him to draw the optimal color solid at an acceptable degree of precision. Because of his achievement, the boundary of the optimal color solid is called the MacAdam limit (1935).
On modern computers, it is possible to calculate an optimal color solid with great precision in seconds. Usually, only the MacAdam limits (the optimal colors, the boundary of the Optimal color solid) are computed, because all the other (non-optimal) possible surface colors exist inside the boundary.
In 1980, Michael R. Pointer published a gamut for real surfaces with diffuse reflection using 4089 samples, (surfaces with specular reflection, "glossy", can fall outside of this gamut).Charles Poynton (2010). "Wide-gamut image capture". Society for Imaging Science and Technology. p. 472. Originally called a "Munsell Color Cascade", the limits are more commonly called Pointer's Gamut after his work.
While this gamut remains important as a reference for color reproduction, newer standards have been created that more accurately define the practical gamut of surfaces, like the ISO SOCS ( Standard Object Colour Spectra), for which 53,361 surfaces were sampled, including paints, prints, flowers, leaves, human faces, textiles, etc; the ISO Reference Colour Gamut (ISORCG, 2007), and the ISO Gamut of Surface Colours (ISOGSC, 1998), which was derived from Pointer’s data, 1025 Pantone samples, printed samples, and ISO SOCS data.
The gamut of a CMYK color space is, ideally, the same as that for an RGB one. In practice, due to the way raster-printed colors interact with each other and the paper and due to their non-ideal absorption spectra, the gamut has rounded corners.
Systems that use additive color processes usually have a color gamut which is roughly a convex polygon (or a slightly concave shape) in a Color difference hue-chroma plane (not to be confused with the chromaticity diagram). The vertices of the polygon are the most chromatic colors that the system can produce.
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