Information about White Balance
In photography and image processing, color balance (sometimes gray balance, neutral balance, or white balance) refers to the adjustment of the relative amounts of red, green, and blue primary colors in an image such that neutral colors are reproduced correctly. Color balance changes the overall mixture of colors in an image and is used for generalized color correction.
Most electronic cameras have a means to select the type of illumination under which the photography is being done. Another option on some cameras is a button which one may press when the camera is looking at a gray card or other neutral object, to capture a "custom" color balance. A very common option is "automatic color balance," which may be based on a scheme such as Retinex, an artificial neural network[1] or a Bayesian method.[2]
Color balance is also related to color constancy. Algorithms and techniques used to attain color constancy are frequently used for color balancing, as well. Color constancy is, in turn, related to chromatic adaptation. Conceptually, color balancing consists of two steps: first, determining the illuminant under which an image was captured; and second, scaling the components (e.g., R, G, and B) of the image or otherwise transforming the components so they conform to the viewing illuminant.
Viggiano[3] found that white balancing in the camera's native RGB tended to produce less color inconstancy (i.e., less distortion of the colors) than in monitor RGB for over 4000 hypothetical sets of camera sensitivities. This difference typically amounted to a factor of more than two in favor of camera RGB. This means that it is advantageous to get color balance right at the time an image is captured, rather than edit later on a monitor. If one must color balance later, balancing the raw image data will tend to produce less distortion of chromatic colors than balancing in monitor RGB.
where
, 
, and 
are the color balanced red, green, and blue components of a pixel in the image; 
, 
, and 
are the red, green, and blue components of the image before color balancing, and 
, 
, and 
are the red, green, and blue components of a pixel which is believed to be a white surface in the image before color balancing. This is a simple scaling of the red, green, and blue channels, and is why color balance tools in Photoshop and the GIMP have a white eyedropper tool. It has been demonstrated that performing the white balancing in the phosphor set assumed by sRGB tends to produce large errors in chromatic colors, even though it can render the neutral surfaces perfectly neutral.[3]
where
, 
, and 
are the color-balanced tristimulus values; 
, 
, and 
are the tristimulus values of the viewing illuminant (the white point to which the image is being transformed to conform to); 
, 
, and 
are the tristimulus values of an object believed to be white in the un-color-balanced image, and 
, 
, and 
are the tristimulus values of a pixel in the un-color-balanced image. If the tristimulus values of the monitor primaries are in a matrix 
so that:
where
, 
, and 
are the un-gamma corrected monitor RGB, one may use:
where
, 
, and 
are the color-balanced LMS cone tristimulus values; 
, 
, and 
are the tristimulus values of an object believed to be white in the un-color-balanced image, and 
, 
, and 
are the tristimulus values of a pixel in the un-color-balanced image.
Matrices to convert to LMS space were not specified by von Kries, but can be derived from CIE color matching functions and LMS color matching functions when the latter are specified; matrices can also be found in reference books.[6]
and then convert to a working RGB space such as sRGB or Adobe RGB after balancing.
The retina is a thin layer of neural cells that lines the back of the eyeball of vertebrates and some cephalopods. It is comparable to the film in a camera.
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Most electronic cameras have a means to select the type of illumination under which the photography is being done. Another option on some cameras is a button which one may press when the camera is looking at a gray card or other neutral object, to capture a "custom" color balance. A very common option is "automatic color balance," which may be based on a scheme such as Retinex, an artificial neural network[1] or a Bayesian method.[2]
Color balance and chromatic colors
Color balancing an image affects not only the neutrals, but other colors as well. An image that is not color balanced is said to have a color cast, as everything in the image appears to have been shifted towards one color or another.[3] Color balancing may be thought in terms of removing this color cast.Color balance is also related to color constancy. Algorithms and techniques used to attain color constancy are frequently used for color balancing, as well. Color constancy is, in turn, related to chromatic adaptation. Conceptually, color balancing consists of two steps: first, determining the illuminant under which an image was captured; and second, scaling the components (e.g., R, G, and B) of the image or otherwise transforming the components so they conform to the viewing illuminant.
Viggiano[3] found that white balancing in the camera's native RGB tended to produce less color inconstancy (i.e., less distortion of the colors) than in monitor RGB for over 4000 hypothetical sets of camera sensitivities. This difference typically amounted to a factor of more than two in favor of camera RGB. This means that it is advantageous to get color balance right at the time an image is captured, rather than edit later on a monitor. If one must color balance later, balancing the raw image data will tend to produce less distortion of chromatic colors than balancing in monitor RGB.
Mathematics of color balance
Color balancing is sometimes performed on a three-component image (e.g., RGB) using a 3x3 matrix. This type of transformation is appropriate if the image were captured using the wrong white balance setting on a digital camera, or through a color filter.Scaling monitor R, G, and B
In principle, one wants to scale all relative luminances in an image so that objects which are believed to be neutral appear so. If, say, a surface with R=240 was believed to be a white object, and if 255 is the count which corresponds to white, one could multiply all red values by 255/240. Doing analogously for green and blue would result, at least in theory, in a color balanced image. In this type of transformation the 3x3 matrix is a diagonal matrix.
where
, , and are the color balanced red, green, and blue components of a pixel in the image; , , and are the red, green, and blue components of the image before color balancing, and , , and are the red, green, and blue components of a pixel which is believed to be a white surface in the image before color balancing. This is a simple scaling of the red, green, and blue channels, and is why color balance tools in Photoshop and the GIMP have a white eyedropper tool. It has been demonstrated that performing the white balancing in the phosphor set assumed by sRGB tends to produce large errors in chromatic colors, even though it can render the neutral surfaces perfectly neutral.[3]
Scaling X, Y, Z
If the image may be transformed into CIE XYZ tristimulus values, the color balancing may be performed there. This has been termed a “wrong von Kries” transformation.[5] Although it has been demonstrated to offer usually poorer results than balancing in monitor RGB, it is mentioned here as a bridge to other things. Mathematically, one computes:
where
, , and are the color-balanced tristimulus values; , , and are the tristimulus values of the viewing illuminant (the white point to which the image is being transformed to conform to); , , and are the tristimulus values of an object believed to be white in the un-color-balanced image, and , , and are the tristimulus values of a pixel in the un-color-balanced image. If the tristimulus values of the monitor primaries are in a matrix so that:
where
, , and are the un-gamma corrected monitor RGB, one may use:
Von Kries's method
Johannes von Kries, whose theory of rods and three different color-sensitive cone types in the retina has survived as the dominant explanation of color sensation for over 100 years, motivated the method of converting color to the "LMS" tristimulus space, representing the effective stimuli for the Long-, Medium-, and Short-wavelength cone types that are modeled as adapting independently. A 3x3 matrix converts RGB or XYZ to LMS, and then the three LMS primary values are scaled to balance the neutral; the color can then be converted back to the desired final color space.[6]
where
, , and are the color-balanced LMS cone tristimulus values; , , and are the tristimulus values of an object believed to be white in the un-color-balanced image, and , , and are the tristimulus values of a pixel in the un-color-balanced image.
Matrices to convert to LMS space were not specified by von Kries, but can be derived from CIE color matching functions and LMS color matching functions when the latter are specified; matrices can also be found in reference books.[6]
Scaling camera RGB
By Viggiano's measure, and using his model of gaussian camera spectral sensitivities, most camera RGB spaces performed better than either monitor RGB or XYZ.[3] If the camera's raw RGB values are known, one may use the 3x3 diagonal matrix:and then convert to a working RGB space such as sRGB or Adobe RGB after balancing.
See also
References
1. ^ Brian Funt, Vlad Cardei, and Kobus Barnard, "Learning color constancy." Proceedings of the Fourth IS&T/SID Color Imaging Conference, p 58-60 (1996).
2. ^ Graham Finlayson, Paul M Hubel, and Steven Hordley, "Color by correlation." Proceedings of the fifth IS&T/SID Color Imaging Conference, p. 6-11 (1997).
3. ^ John A C Yule, Principles of Color Reproduction. New York: Wiley, 1967.
4. ^ J A Stephen Viggiano, "Comparison of the accuracy of different white balancing options as quantified by their color constancy." Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications V: Proceedings of the SPIE, volume 5301. Bellingham, WA: SPIE: the International Society for Optical Engineering, p 323-333 (2004), retrieved online 2007-05-15 from [1]
5. ^ Mark D Fairchild, Color Appearance Models. Reading, MA: Addison-Wesley, 1998.
6. ^ Gaurav Sharma (2003). Digital Color Imaging Handbook. CRC Press. ISBN 084930900X.
2. ^ Graham Finlayson, Paul M Hubel, and Steven Hordley, "Color by correlation." Proceedings of the fifth IS&T/SID Color Imaging Conference, p. 6-11 (1997).
3. ^ John A C Yule, Principles of Color Reproduction. New York: Wiley, 1967.
4. ^ J A Stephen Viggiano, "Comparison of the accuracy of different white balancing options as quantified by their color constancy." Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications V: Proceedings of the SPIE, volume 5301. Bellingham, WA: SPIE: the International Society for Optical Engineering, p 323-333 (2004), retrieved online 2007-05-15 from [1]
5. ^ Mark D Fairchild, Color Appearance Models. Reading, MA: Addison-Wesley, 1998.
6. ^ Gaurav Sharma (2003). Digital Color Imaging Handbook. CRC Press. ISBN 084930900X.
External links
- Photoshop CS Tutorial listings, Color Balance, Color Selection, and more
- Correction of color balance in photography (GPL Gimp)
- Using Illumination Information in Color Balance Adjustments
- Television Production Color Balancing Cameras
- How do I adjust color balance in Photoshop?
- PhotoNotes.org Dictionary - Colour/color balance
Photography [fә'tɑgrәfi:],[foʊ'tɑgrәfi:] is the process of recording pictures by means of capturing light on a light-sensitive medium, such as a film or electronic sensor.
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Image processing is any form of information processing for which the input is an image, such as photographs or frames of video; the output is not necessarily an image, but can be for instance a set of features of the image.
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Primary Colors: A Novel of Politics
Author Joe Klein
Country United States
Language English
Genre(s) Political novel
Publisher Random House
Publication date January 16, 1996
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Author Joe Klein
Country United States
Language English
Genre(s) Political novel
Publisher Random House
Publication date January 16, 1996
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Color correction by using color gels, is a process used in stage lighting, photography, television, cinematography and other disciplines, the intention of which is to alter the overall quality of the light measured on a scale known as color temperature.
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Gray cards are used, together with reflective light meters, as a way to produce consistent images in film and photography.
A gray card is a flat object of a neutral gray color that derives from a flat reflectance spectrum.
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A gray card is a flat object of a neutral gray color that derives from a flat reflectance spectrum.
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Color constancy is an example of subjective constancy and a feature of the human color perception system which ensures that the perceived color of objects remains relatively constant under varying illumination conditions.
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An artificial neural network (ANN), often just called a "neural network" (NN), is a mathematical model or computational model based on biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach
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Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. The name "Bayesian" comes from the frequent use of Bayes' theorem in the inference process.
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Color constancy is an example of subjective constancy and a feature of the human color perception system which ensures that the perceived color of objects remains relatively constant under varying illumination conditions.
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Color vision is the capacity of an organism or machine to distinguish objects based on the wavelengths (or frequencies) of the light they reflect or emit. The nervous system derives color by comparing the responses to light from the several types of cone photoreceptors in the eye.
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RGB color model is an additive model in which red, green, and blue (often used in additive light models) are combined in various ways to reproduce other colors. The name of the model and the abbreviation ‘RGB’ come from the three primary colors, red, green, and blue and
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RAW image file
File extension:
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File extension:
.raf (Fuji)
.crw .cr2 (Canon)
.kdc .dcr (Kodak)
.mrw (Minolta)
.nef (Nikon)
.orf (Olympus)
.dng (Adobe)
.ptx .pef (Pentax)
.arw .srf (Sony)
.x3f (Sigma)
.erf (Epson)
.mos (Leaf)
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RGB color model is an additive model in which red, green, and blue (often used in additive light models) are combined in various ways to reproduce other colors. The name of the model and the abbreviation ‘RGB’ come from the three primary colors, red, green, and blue and
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matrix (plural matrices) is a rectangular table of elements (or entries), which may be numbers or, more generally, any abstract quantities that can be added and multiplied.
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Grey or gray (see spelling differences) describes any color between black and white. Collectively, white, black, and the range of greys between them are known as achromatic colors or neutral colors. Greys are seen commonly in nature and fashion.
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Red is any of a number of similar colors evoked by light consisting predominantly of the longest wavelengths of light discernible by the human eye, in the wavelength range of roughly 625–750 nm.
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Green is a color, the perception of which is evoked by light having a spectrum dominated by energy with a wavelength of roughly 520–570 nm. It is considered one of the additive primary colors.
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The term blue may refer to any of a number of similar colours. The sensation of blue is made by light having a spectrum dominated by energy in the wavelength range of about 440–490 nm.
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In linear algebra, a diagonal matrix is a square matrix in which the entries outside the main diagonal are all zero. The diagonal entries themselves may or may not be zero.
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pixel (short for picture element, using the common abbreviation "pix" for "pictures") is a single point in a graphic image. Each such information element is not really a dot, nor a square, but an abstract sample.
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Adobe Photoshop, or simply Photoshop, is a graphics editor developed and published by Adobe Systems. It is the current market leader for commercial bitmap and image manipulation, and is the flagship product of Adobe Systems.
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Gimp may be or refer to:
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- A person with a limp or other physical disability in the legs, e.g. paraplegia
- The GNU Image Manipulation Program, an open-source, image editing program
- GIMPS, the Great Internet Mersenne Prime Search
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sRGB is a standard RGB (Red Green Blue) color space created cooperatively by HP and Microsoft for use on monitors, printers, and the Internet. It was originally proposed in 1995 by Ralf Kuron of FOGRA as a pragmatic approach in connection to ICC.
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CIE 1931 XYZ color space (also known as CIE 1931 color space), created by the International Commission on Illumination (CIE) in 1931.
The human eye has receptors (called cone cells) for short (S), middle (M), and long (L) wavelengths.
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The human eye has receptors (called cone cells) for short (S), middle (M), and long (L) wavelengths.
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Gamma correction, gamma nonlinearity, gamma encoding, or often simply gamma, is the name of a nonlinear operation used to code and decode luminance or tristimulus values in video or still image systems.
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Johannes von Kries (1853–1928) was a German "physiological psychologist"[1] who formulated the modern "duplicity" or "duplexity" theory of vision mediated by rods at low light levels and three types of cones at higher light levels.
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- For the moth genus, see Retina (moth).
The retina is a thin layer of neural cells that lines the back of the eyeball of vertebrates and some cephalopods. It is comparable to the film in a camera.
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color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB model.
In the most generic sense of the definition above, color spaces can be defined without the use of a color model.
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In the most generic sense of the definition above, color spaces can be defined without the use of a color model.
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sRGB is a standard RGB (Red Green Blue) color space created cooperatively by HP and Microsoft for use on monitors, printers, and the Internet. It was originally proposed in 1995 by Ralf Kuron of FOGRA as a pragmatic approach in connection to ICC.
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Adobe RGB color space is an RGB color space developed by Adobe Systems in 1998. It was designed to encompass most of the colors achievable on CMYK color printers, but by using RGB primary colors on a device such as the computer display.
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