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
Hi Parker, just found this a bit late but it’s very, very useful! I’ve had a few ‘arguments’ with folks who swear their cameras have a color gamut, it’s great to have this outside reference to show them otherwise. Your article should be easily understood by color management newbies.
Hi Andrew,
Thank you for the positive feedback. You have been a great educator and source of sound information for two decades. And I appreciate the help you provided when I was at Epson. Keep up the good work.
Sincerely,
Parker
Looks like RIT link is now changed to https://www.rit.edu/cos/colorscience/rc_faq_faq3.php#255
From the point of view of math I’m using in my profiling tools, an input device is described the same way we normally describe observers.
Hi Parker.
Nice to come across your very well crafted site. I have a question/comment on the matter of whether or not digital cameras have a color gamut. If, using the CIE color matching functions (1931 or 1964, take your pick) and a CIE broadband Illuminant (D series, A, take your pick), I calculate the chromaticity values of all points, then I will recreate a CIE chromaticity diagram. Adding the purple line, closes the open end.
Clearly, I can do the same thing using the spectral response functions of a digital camera and produce a similar graph (although the shapes will differ somewhat between that from the camera and from the CIE color matching functions).
In what way are either of these constraining convex shapes (whether using the CIE color matching functions or camera spectral responsively functions) not a “gamut”. Yes, they have more than 3 points, but I’ve never heard that the definition of a color gamut is limited by the precision with which its shape is specified.
Similarly, if I had a printer with a massive number of highly saturated colorants (lets say for arguments sake each only reflected one wavelength) and I had several hundred of them, such that the entire set reflected wavelengths from 360 to 830 nm. How would the gamut of that printer differ from the CIE chromaticity diagram?
I believe they would be identical? If so, I think the definition of a gamut is ill served by limiting it by considering only the area (or volume) constrained by devices having an arbitrarily small number of colorants.
I suggest that the notion of a color gamut be the range of colors that a device can represent, whether that device is a printer, display, camera, or the human visual system.
You can of course argue that the concept of a gamut is not meaningful in describing the human visual system, since color itself is not defined outside of the range of sensations we can experience within the range of visible light. If so, what about people whose visual sensitivity extends outside of the normal range of 360-780 nm such as people who have aphakia? I’m not sure what human CMF are between 300-360 nm are for people with aphakia, but they might have color sensitivity that extends past normal observers. Would not a chromaticity diagram of such a person contain a larger area than that of the CIE standard observer and thus, be a larger “gamut”.
And carrying the parallel to cameras (although in a different direction spectrally), does a digital camera without an IR-cut filter not have a greater gamut than one with an IR-cut filter?
I’d be interested in your thoughts about my questions/comments.
–Seth
Hi Seth,
It is great to hear from you, my friend. It has been a long time since we were students at RIT.
I agree that a definition of color gamut is not constrained by the precision with which its shape is specified.
I want to be careful in my response because I am no longer active in the color science community and my insights are less keen these days.
I think the simple view of the distinction of associating color gamuts with devices that produce color versus devices that measure light (and radiation in the UV and IR ranges) is the behavior at the boundaries. Measurement devices will saturate at the boundaries limiting the results of different stimuli to redundant digital values. That is different from the purity of colors at the boundaries of a gamut of rendered colors (e.g., printer color gamut).
I think your point about the CIE chromaticity diagram has merit. I also think the scientific community can benefit from having a model for describing the range of colors a measurement device (e.g., digital camera) can capture. Unfortunately, I know that I am not insightful enough to contribute to that conversation.
Sincerely,
Parker
I asked it to Mark D. Fairchild from RIT if cameras have a color gamut and I just would like to share his answer here:
“This one is easy for me … cameras absolutely do not have gamuts.
A color gamut is the range of colors produced by a device or system. I can take an image from any camera and produce any colors I like.
So I fall strongly, and unequivocally, on the side that says cameras do not have color gamuts. (FWIW, this isn’t even a discussion among the faculty in our program, we all agree on this.)
The human eye also does not have a gamut. The spectral locus on chromaticity diagram (which is also missing a dimension) simply shows the response of the eye to monochromatic light. The limit is in the light, not the eye. The camera can also respond to all that light.”
Thank you, Andrew. I appreciate this additional information.