Category: Color Science

Color Science

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).

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.

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. 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. 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 https://www.rit.edu/cos/colorscience/rc_faq_faq3.php#255

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.

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

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.

The CIELAB Reference White

One of the important factors in calculating color coordinates in the CIELAB color space is the reference white. The two primary inputs to the CIELAB equations are the set of CIE XYZ tristimulus values for the stimulus, or measured color, and the CIE XYZ tristimulus values for the reference white. The CIE does not identify a specific reference white for CIELAB, so any appropriate reference white may be used (e.g., D50). Therefore, it is important to state the reference white when using or reporting CIELAB color coordinates in order to avoid a misinterpretation of the CIELAB values.

The benefit gained from the reference white in the CIELAB equations is the chromatic adaptation provided by dividing each CIE XYZ tristimulus value of the stimulus by the corresponding CIE XYZ tristimulus value for the reference white (i.e., X/Xn, Y/Yn, and Z/Zn where Xn, Yn, and Zn are the CIE XYZ tristimulus values for the reference white).

This chromatic adaptation in CIELAB based on the CIE XYZ tristimulus values is an approximation to the von Kries chromatic adaptation model, which is applied to the retinal cone responses, but it is less accurate than a proper von Kries adaptation. A proper von Kries chromatic adaptation adjustment would require a transformation from CIE XYZ tristimulus values to cone responses, scaling of the cone responses, and then a transformation of the scaled cone responses back to CIE XYZ tristimulus values. Applying the adaptation scaling directly to the CIE XYZ tristimulus values is easier to implement than a proper von Kries adaptation and provides adaptation results that were deemed sufficient by the CIE in 1976 for color difference calculations with the CIELAB equations.

A chromatic adaptation model provides a means to account for the observer’s visual adaptation to the illumination of a measured color. This is most apparent in the CIELAB color coordinates when the measured color is white. For example, when the CIE XYZ tristimulus values for the stimulus and the reference white are the same, then the CIELAB values are L* = 100, a* = 0, and b* = 0. Under this unique condition, when the CIE XYZ tristimulus values for the stimulus and the reference white are the same, the observer is expected to be visually adapted to the reference white.

This chromatic adaptation attribute of the CIELAB color space is the reason why it is important to state the reference white when using or reporting CIELAB color coordinates. Interpretation of any given color coordinates in the CIELAB color space is relative to the reference white that was used in the calculations of the CIELAB color coordinates.

Post written by Parker Plaisted

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

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

Colorimetry, second edition. CIE Publication 15.2 (1986).

The Proper Notation for the CIELAB Color Space

The 1976 CIELAB color space is 3-dimensional with the dimensions labeled as L*, a*, and b*. Unfortunately, some implementations of the CIELAB color space in software applications have not followed the proper notation and have simply labeled the three dimensions as L, a, and b. To people outside of the color science community, this may appear to be a trivial difference that does not alter the meaning of the color space. But to people within the color science community, the difference is not trivial.

Here is a brief history of two of the many steps that led to the introduction of the 1976 CIELAB color space and the CIE L*, a*, and b* coordinates.

In 1948, Richard S. Hunter published a paper in which he described a photoelectric color-difference meter that would measure color and deliver 3 values to quantify the color. The axes in this three-dimensional, Cartesian coordinate system where labeled L, a, and b. This color space is known as the Hunter 1948 Lab color space, and the calculations for L, a, and b are based on measurement of the CIE 1931 XYZ tristimulus values with the photoelectric color-difference meter.

The Hunter 1948 Lab color space incorporates the opponent-colors theory—proposed by Ewald Hering in 1878—with the a-axis representing the redness or greenness of a color and the b-axis representing the yellowness or blueness of a color. The L-axis represents the perceived lightness of the color.

Building upon the Lab Cartesian coordinate system for a color space, Glasser, McKinney, Reilly, and Schnelle published a paper in 1958 in which they described a visually uniform color coordinate system and the use of cube-root functions to calculate the values for the three dimensions of the color space. They used L*, a*, and b* to denote the three dimensions. Although the notations and goals were similar, the equations for L*, a*, and b* had very little in common with the Hunter 1948 Lab equations.

There were several other sets of equations that were proposed for the determination of color differences by other scientists, but for the sake of brevity I will not go into those details. However, the existence and use of so many sets of equations prompted the CIE to develop and recommend one set of equations for the calculation of color differences. The result of this effort was the CIE 1976 L*a*b* color space in which the CIE accepted the opponent-colors theory, adopted the Lab approach to the notation for the three axes, and incorporated the cube-root approach proposed by Glasser et al. for the nonlinearity between physical energy measurements and perceptual responses. Recognizing the previous use of Lab for the Hunter 1948 Lab color space and the use of L*, a*, and b* by Glasser et al., the CIE denoted their uniform color space with their initials, the year, and the three axes: CIE 1976 L*a*b*. Some publications added another formality and enclosed the axes in parentheses: CIE 1976 (L*a*b*).

I hope from this explanation that you can see that the Lab notation refers to the Hunter 1948 Lab color space, and the L*a*b* notation refers to the equations proposed by Glasser et al. To avoid confusion, and disdain from color scientists, please use CIE 1976 L*a*b*, or the alternative CIELAB notation, when referring to the CIE 1976 (L*a*b*) uniform color space.

Post written by Parker Plaisted

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

Colorimetry, second edition. CIE Publication 15.2 (1986).

Hunter, R. S., Photoelectric Color-Difference Meter, J. Opt. Soc. Am. 38, 661 (1948).

Glasser, L. G., McKinney, A. H., Reilly, C. D., and Schnelle, P. D., Cube-Root Color Coordinate System, J. Opt. Soc. Am. 48, 736 (1958).

Hering, E., Zur Lehre vom Lichtsinne, Carl Gerold’s Sohn, Wien (1878).