Color Temperature and Correlated Color Temperature CCT

People sometimes get confused about the meaning of correlated color temperature (CCT) and the relationship of this metric to color temperature and the D series of CIE standard illuminants (e.g., D50 and D65). I will offer some scientific insight here to help explain the differences.

  • Each color temperature (e.g., 5000 K) is a single point on the Planckian locus in a chromaticity diagram (e.g., CIE 1931 (x, y) chromaticity diagram).
  • Each CIE standard illuminant in the D series (e.g., D65) is a single point on the CIE daylight locus in a chromaticity diagram (e.g., CIE 1931 (x, y) chromaticity diagram).
  • Each correlated color temperature (e.g. 6504 K CCT) is not a single point in a chromaticity diagram. Many points in a chromaticity diagram can have the same correlated color temperature.

The color temperature of light is based on the concept of the black-body radiator, also known as a Planckian radiator, and the Planckian locus on a chromaticity diagram. The unit of measurement of color temperature is kelvin (e.g., 6500 kelvin or 6500 K). Each unit of color temperature has a corresponding set of chromaticity coordinates on a chromaticity diagram, and those chromaticity coordinates are on the Planckian locus.

The ability to associate the two-dimensional chromaticity coordinates with the one-dimensional scale of color temperature along the Planckian locus enables a simpler communication of the visual appearance of nearly-white light. A given color temperature (e.g., 6500 K) gives us an understanding of the light relative to other color temperatures (e.g., 5500 K or 7500 K). A higher color temperature is more blue in appearance. A lower color temperature is more red in appearance.

Since all color temperatures are restricted to the Planckian locus, we have a problem when we want to use the color temperature scale to communicate the visual appearance of nearly-white light that comes from a light source that produces a spectral power distribution that is different from a black-body radiator. Many light sources — particularly fluorescent lights — produce a spectral power distribution that is different from a black-body radiator.

Well, the color temperature scale was deemed too useful to be restricted to black-body radiators and the Planckian locus. The solution is a less restrictive correlated color temperature (CCT) scale. The correlated color temperature scale is based on the color temperature scale and isotemperature lines, which were proposed by D. B. Judd in 1936. All colors along a given isotemperature line have the same correlated color temperature. Here is Judd’s proposal for isotemperature lines:

“ The estimation of nearest color temperature has been facilitated by the preparation of a mixture diagram on which is shown a family of straight lines intersecting the Planckian locus; each straight line corresponds approximately to the locus of points representing stimuli of chromaticity more closely resembling that of the Planckian radiator at the intersection than that of any other Planckian radiator. ” (771)

The CIE adopted Judd’s proposal for isotemperature lines, but the concept was updated with the CIE 1960 uniform chromaticity scale (UCS) diagram, which was not available in 1936. The isotemperature lines are perpendicular to the Planckian locus in the CIE 1960 UCS diagram, but not in other color spaces (Note: The CIE 1960 UCS diagram has some advantages over other color spaces, and the ease of calculating isotemperature lines is one of them). The chromaticities of the CIE 1960 UCS diagram are denoted by u and v to distinguish the color space from the CIE 1931 (x, y) chromaticity diagram. The CIE provides equations that enable a conversion of chromaticity coordinates from one CIE color space to another. Therefore, we use the convenience of the perpendicular relationship in the CIE 1960 UCS diagram to determine the correlated color temperature of a light source by plotting the isotemperature line from a given set of u, v chromaticity coordinates for the light source to the Planckian locus. We can also translate the coordinates of any given isotemperature line in the CIE 1960 UCS diagram to any other color space that is useful.

The correlated color temperature scale has been, and continues to be, a useful means for companies to describe light sources and for users to specify lighting requirements. But there has been some confusion because the metric is not precise and is not comprehensive. For example, light sources with the same correlated color temperature can deliver different color rendering indices (CRI). If you are working in an environment where color rendering is important, then I recommend getting three metrics for a light source: 1) the correlated color temperature, 2) the white-point chromaticities in one of the CIE chromaticity diagrams, and 3) the color rendering index.

In summary, here is another way to describe the difference between color temperature and correlated color temperature:

  • Color temperature is a metric used to describe a color of light on the Planckian locus and produced from a Planckian radiator. This is a rather limited metric because it is only applicable to a color of light from a Planckian radiator. Each unit of color temperature has one set of chromaticity coordinates in a given color space, and that set of coordinates is on the Planckian locus.
  • Correlated color temperature is a metric used to describe a color of light located near the Planckian locus. This metric has broader utility because it is applicable to a variety of manufactured light sources, where each light source produces a spectral power distribution that is different from a Planckian radiator. However, it is less precise than the color temperature metric because many points in a chromaticity diagram along an isotemperature line will have the same correlated color temperature.

I will close with the description of correlated color temperature in CIE Publication 15.2:

“ The correlated color temperature of a given stimulus is the temperature of the Planckian radiator whose perceived colour most closely resembles that of the stimulus at the same brightness and under the same viewing conditions. ” (38)

Post written by Parker Plaisted

References:
D. B. Judd, “Estimation of Chromaticity Differences and Nearest Color Temperature on the Standard 1931 I.C.I. Colorimetric Coordinate System,” Journal of Research Nat. Bureau Standards, Vol. 17, 771-779 (1936).

Colorimetry, second edition. CIE Publication 15.2 (1986)

G. Wyszecki and W. S. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae, John Wiley & Sons, New York, N.Y. (1986).

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A Digital Camera Does Not Have A Color Gamut

Color gamut is a popular concept in digital color management, and is frequently mentioned in discussions about the selection of a color space (e.g., sRGB or ProPhoto RGB) or the compression of colors in a color-managed workflow. Color gamut volume and color gamut boundary colors are the two aspects of a color gamut that get the most attention, and both provide useful information.

Unfortunately, the concept of a color gamut has been applied to color imaging devices that do not actually have a color gamut. Only devices, or systems, that render color have a color gamut. To quote Dr. Roy S. Berns from RIT in the book Billmeyer and Saltzman’s Principles of Color Technology, "Color gamut: Range of colors produced by a coloration system." To be a little clearer about this, the concept of a color gamut applies to systems that produce color (e.g., color printer, color television, color monitor, or color projector).

The concept of a color gamut is not relevant to systems, or devices, that measure color. In the context of digital color imaging, a color measurement device is exposed to colored light and delivers a set of digital values to represent that colored light. The obvious examples are colorimeters and spectrophotometers, which are used in scientific color measurement work. Digital color cameras and scanners are also color measurement devices. These devices do not render or produce color, they measure color. Therefore, none of them have a color gamut.

We can characterize a color measurement device, with some constraints on the exposure conditions, and use that characterization in an ICC profile for that device (e.g., an ICC profile for a digital color camera or a color film scanner). But that characterization is not the same as a color gamut. The characterization may look a lot like a color gamut in a software tool that displays color gamuts and device characterizations, and that may be the reason why people think the concept of a color gamut is relevant for digital color cameras.

Another contributing factor to the confusion is the option on a digital color camera to choose an RGB color space (e.g., sRGB, Adobe RGB (1998), or ProPhoto RGB) for the encoding of a photograph within the digital color camera. These RGB color spaces are convenient color spaces that simplify color management of a digital photograph downstream from the digital camera. Encoding a digital photograph in one of these RGB color spaces will constrain the digital photograph to the gamut of the color space (Yes, each of these RGB color spaces has a color gamut that is constrained by the colorimetric values of the red, green, and blue primaries of the color space). It will also tie the digital photograph to the white point of the color space and establish the digital resolution within the color space (e.g., 8-bits per channel or 16-bits per channel). But the selected RGB color space is not the color gamut of the digital camera. If this distinction is not obvious after you have read the entire blog post, please leave a comment and I will go into more detail.

If we cannot apply the concept of a color gamut to a color measurement device, then how do we describe the capabilities and limitations of the color measurement device? The proper way to describe the capabilities and limitations of a color measurement device is to provide the color-matching functions of the device. The color-matching functions quantitatively describe the spectral sensitivities of the separate color sensors (e.g., red, green, and blue filters over separate light detectors). This becomes a little more obvious when you think about the color-matching functions for human vision. Human vision is a color measurement system, and we use the color-matching functions of the CIE 1931 standard colorimetric observer, or the CIE 1964 supplementary standard colorimetric observer, to quantify measured colors.

I recognize that it is easier to understand color management when we can see the color gamut of each device displayed in the same color space. Unfortunately, we cannot display the color gamut of a digital color camera in CIELAB space, or the CIE xy chromaticity space, for comparison with the color gamut of a color monitor or a color printer because the digital color camera does not have a color gamut. Furthermore, a diagram of the color-matching functions of a digital color camera lends very little insight when compared to a 2-D or 3-D rendering of the color gamut of a color monitor or a color printer. So I am sympathetic with the desire to give a digital color camera a color gamut in order to facilitate a comparison to color rendering devices. The good news is that we have a simple solution: device characterization with a common colorimetric color space (e.g., CIELAB).

In the practical application of a color management system, the characterization of a color-imaging device is the information that enables color management. This is true for any color rendering device and any color measurement device in the digital color workflow. The data within an ICC profile are based on characterization data, not the limits of a color gamut or the color-matching functions. The information taken from an ICC profile and rendered by software tools to visualize the color volume and boundaries of the color-imaging device is based on the characterization data. We should keep this in mind when someone incorrectly talks about the color gamut for a digital camera. We know that a digital color camera does not have a color gamut, but we can talk about the characterization of a digital camera, or the selection of a standard RGB color space within the camera, and frame the discussion in that context.

Post written by Parker Plaisted

References:
R. S. Berns, Billmeyer and Saltzman’s Principles of Color Technology, 3rd Edition, John Wiley & Sons, New York, N.Y. (2000).

International Color Consortium, ICC Profile Format Specification. (http://www.color.org)

Imaging FAQ on the RIT CIS Munsell Color Science Laboratory (MCSL) Website http://www.cis.rit.edu/mcsl/faq3#255

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Comparison of Adobe RGB and sRGB Colors

Two of the most commonly used ICC profiles for RGB images are Adobe RGB (1998) and sRGB IEC61966-2.1. The Adobe RGB (1998) color space has a larger color gamut than the sRGB IEC61966-2.1 color space, but you may be surprised to see that they share many similarities.

In order to compare the two color spaces, I will use the CIE and ICC data that define the color spaces:

  1. Gamma value
  2. White point
  3. Red primary CIE chromaticity coordinates
  4. Green primary CIE chromaticity coordinates
  5. Blue primary CIE chromaticity coordinates

Adobe RGB and sRGB attribute values

As you can see in the table, Adobe RGB (1998) and sRGB IEC61966-2.1 share the same values for four of the five attributes. The only difference is the set of CIE chromaticity coordinates for the green primary.

Now let me show you two versions of a sample image. For the first image, the sRGB IEC61966-2.1 ICC profile was assigned to the image in Adobe Photoshop. For the second image, the Adobe RGB (1998) ICC profile was assigned to the image in Adobe Photoshop. (Note: To make sure you see a difference between the two images, I converted the second image from Adobe RGB (1998) to sRGB IEC61966-2.1 so that both images are coded to the same color space. Yes, I could have coded both of them in the Adobe RGB (1998) color space to make sure no colors got clipped. Feel free to repeat this experiment in a color-managed display environment to see the color differences.)

sRGB IEC61966-2.1 ICC Profile
food image sRGB

Adobe RGB (1998) ICC Profile
food image Adobe RGB

As you can see, the colors in the image with the sRGB IEC61966-2.1 ICC profile are significantly different from the colors in the image with the Adobe RGB (1998) ICC profile.

Based on the data comparison above, I would have expected the reds and blues to be similar in both images, and I would have expected the greens to be very different. However, the greens and blues have small shifts in color, and the reds and oranges have large shifts in color. Here is another set of images that demonstrates the color differences.

sRGB IEC61966-2.1 ICC Profile
park slide image sRGB

Adobe RGB (1998) ICC Profile
park slide image Adobe RGB

Why do we see these large color differences in the reds and oranges when the only difference in the two color spaces is the set of CIE chromaticity coordinates for the green primary? The answer can be found in the CIE XYZ tristimulus values for the red, green, and blue primaries.

sRGB IEC61966-2.1 CIE XYZ Tristimulus Values
sRGB CIE XYZ tristimulus values

Adobe RGB (1998) CIE XYZ Tristimulus Values
Adobe RGB CIE XYZ tristimulus values

The two color spaces share the same CIE chromaticity coordinates for the red and blue primaries, and share the same D65 white point and 2.2 gamma value, but the two sets of CIE XYZ tristimulus values are completely different.

To fully understand the differences when the images are viewed with proper color management, we have to look at the 3×3 matrix in the ICC profile. As I described in an earlier post, a chromatic adaptation transform must be applied to the CIE XYZ tristimulus values to force the 3×3 matrix to deliver the D50 white point that is required in the specification for ICC profiles. For the Adobe RGB (1998) and sRGB IEC61966-2.1 ICC profiles, the Bradford transform and proper von Kries scaling were used to move the white point from D65 to D50. The CIE XYZ tristimulus values in the 3×3 matrices are shown below.

sRGB IEC61966-2.1 ICC Profile 3×3 Matrix
sRGB ICC 3x3 matrix values

Adobe RGB (1998) ICC Profile 3×3 Matrix
Adobe RGB ICC 3x3 matrix values

The CIE XYZ tristimulus values in these two 3×3 matrices help explain the color shifts seen in the two sets of example images. Further analysis can be done with these two matrices to compare the CIELAB values associated with a given set of RGB pixel values.

The point of this post is to alert people to be careful when comparing color spaces in color managed workflows. It is convenient to compare RGB color spaces based on the CIE chromaticity coordinates of the primaries, but it is difficult to predict the color differences in color-managed images from a comparison of CIE chromaticity coordinates of the primaries.

Post written by Parker Plaisted

References:
International Color Consortium, ICC Profile Format Specification, http://www.color.org

M. Fairchild, Color Appearance Models, Addison-Wesley, Reading, Massachusetts (1998).

Bruce Lindbloom Website, RGB Working Space Information page, http://www.brucelindbloom.com

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Chromatic Adaptation for Display Profiles

When I was working on improvements to the OptiCAL software in 1998, one of the challenges was the selection of the chromatic adaptation algorithm. At the time, the International Color Consortium (ICC) did not specify a chromatic adaptation algorithm, so each software developer could choose any method for chromatic adaptation.

Let me back up and give you a little more context for this issue. The ICC had specified CIE D50 for the white point of the Profile Connection Space (PCS). Therefore, all the color coding in the PCS had to have a white point of D50. That worked well for output profiles (e.g., printer profiles), but it created an extra step in the creation of display profiles (e.g., monitor profiles) and RGB color-space profiles when the native white point was not D50. That extra step was the application of a chromatic adaptation transform.

I will focus on an ICC display profile for a color monitor as an example. When the display profile is based on the matrix structure, rather than the LUT structure, the 3×3 matrix contains CIE XYZ tristimulus values for the red, green, and blue primaries. By following the rules of linear algebra, the sum of each row in the 3×3 matrix produces the CIE X, Y, and Z tristimulus values for the white point of the display (i.e., the sum of the top row delivers the X value, the sum of the middle row delivers the Y value and the sum of the bottom row delivers the Z value). To be compliant with the ICC specification, the 3×3 matrix must deliver the CIE XYZ tristimulus values of D50 for the CIE XYZ tristimulus values of white (i.e., white XYZ for the display profile = CIE D50 XYZ).

If the white point of the actual display is not D50, then a chromatic adaptation transform must be applied to the measured data to force the 3×3 matrix to deliver the CIE XYZ tristimulus values for D50. (Keep in mind that a display will rarely be calibrated to exactly D50, so a chromatic adaptation transform will be applied to the measured data even for a small deviation from D50.) In simple terms, the chromatic-adaptation transform changes the tristimulus values of the red, green, and blue primaries to produce a D50 white from the sum of the three primaries. The new tristimulus values for the red, green, and blue primaries are placed in the 3×3 matrix for the display profile.

Without a specification from the ICC for a chromatic adaptation algorithm (CAT), software developers were free to choose any chromatic adaptation algorithm. Some software developers simply used linear scaling on the measured CIE XYZ tristimulus values for the red, green, and blue primaries to force them to deliver the CIE XYZ tristimulus values of D50 for white. This technique is generally referred to as a “wrong von Kries transform” and the results are inferior to a proper von Kries transform. In 1998, the two popular chromatic adaptation transforms were the Hunt Pointer Estevez transform and the Bradford transform. Each of these three methods (i.e., linear scaling of the XYZ values, von Kries scaling with the Hunt Pointer Estevez transform, and von Kries scaling with the Bradford transform) produced different sets of XYZ tristimulus values for the 3×3 matrix, and, in turn, delivered different colors when used in a color management pipeline. At the time, most people using color management were unaware of this little detail in display profiles, and to this day I still encounter “color management experts” who are not aware of this little detail. Fortunately the ICC addressed this issue and now recommends the Bradford transform for Version 4 of the ICC profile specification. Software developers who create software applications that make ICC profiles should now use the Bradford transform in the software in order for the ICC profiles to be compliant with the Version 4 specification, but exceptions are allowed and can be implemented in the ICC profile and noted in the chromatic adaptation tag in the ICC profile. Let me quote Annex E in Specification ICC.1:2010 (Profile version 4.3.0.0):

The ICC profile format specification allows the use of different linear (matrix-based) CATs. This flexibility allows profile creators to select the most appropriate CAT for their applications. Criteria for selection include visual performance, the gamut of the image as transformed to the PCS, and other considerations. However, the use of different CATs will produce different results, which may be undesirable. Therefore, it is recommended that the linear Bradford CAT be used when there is no reason to use a different CAT. The linear Bradford CAT has been widely implemented in the digital imaging industry, with demonstrated excellent visual performance. If a profile creator decides to use a CAT other than linear Bradford, they should do so only to address specific known issues, recognizing that the resulting profile will most likely produce different results than profiles from other sources.

In 1998 I chose the Hunt Pointer Estevez transform based on discussions with colleagues who had significant experience with chromatic adaptation transforms (CATs) and were familiar with the Hunt Pointer Estevez transform and the Bradford transform. Working with Dana Gregory on the OptiCAL software, we implemented the Hunt Pointer Estevez transform for OptiCAL version 2.5. Five years later in 2003, the OptiCAL software was updated to use the Bradford transform for chromatic adaptation to conform to the Version 4 ICC Specification. The update to the OptiCAL software that incorporated the Bradford transform was clearly the right decision, and Michael Brill provided a detailed report on this update to the software in 2003.

I hope this post will elevate awareness of the presence of a chromatic adaptation transform in software that produces display profiles. I will visit this topic again in future posts to share additional insights on related issues that may be overlooked in color management workflows.

Post written by Parker Plaisted

References:
International Color Consortium, ICC Profile Format Specification. (http://www.color.org)

M. Fairchild, Color Appearance Models, Addison-Wesley, Reading, Massachusetts (1998).

Posted in Monitor Calibration | Tagged , , , | 1 Comment

RIT MCSL Industrial Short Courses 2012

The Munsell Color Science Lab at the Rochester Institute of Technology (RIT) is offering Industrial Short Courses again this year in June. The instructors are faculty and staff at the Munsell Color Science Lab. A short description of each instructor is provided on the RIT site.

Fundamentals of Color Science
June 5-6, 2012
A two-day short course composed of eight lectures focused on the theory and application of modern color science.

  1. Understanding Color (Mark Fairchild)
  2. Color Vision (James Ferwerda)
  3. CIE Color Spaces (Roy Berns)
  4. Color Measurements (Dave Wyble)
  5. Setting Color Tolerances (Roy S. Berns)
  6. Beyond Color: Gloss and Texture (James Ferwerda)
  7. Color and Illumination (Mark Fairchild)
  8. Color Imaging (Jinwei Gu)

Advanced Topics in Color and Imaging
June 7, 2012
A one-day course that covers four advanced topics in color and imaging science.

  1. Color Appearance (Mark Fairchild)
  2. Image Appearance (Mark Fairchild)
  3. Psychophysical Methods in Color Science (James Ferwerda)
  4. Surface Appearance Capture and Rendering (Jinwei Gu)

Instrumental-Based Color Matching
June 7, 2012
A hands-on, one-day course with both lectures and laboratories where you will gain a deeper understanding of commercial matching systems. The course is taught by Dr. Roy Berns.

  1. Optical Models for Reflecting Materials
  2. Colorant Database Development and Evaluation
  3. Spectral and Colorimetric Matching Algorithms
  4. Matching Evaluation and Batch Correction

More information about each of the courses is provided on the Industrial Short Courses page on the RIT site.

Personal note:
As a former student in the Munsell Color Science Lab at RIT many years ago, I can tell you that these are outstanding classes. Dr. Roy Berns and Dr. Mark Fairchild were my professors, and they are both very knowledgeable and entertaining in the classroom.

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