How can I compare and match colors between a digital image and a printed T-shirt photo?
Asked 7/15/2021
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2 answers
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I print digital images onto T-shirts, then photograph the printed shirt and compare it with the original file. The photographed print looks different in its color histogram, which is expected.
I want to know whether there is a standard way to model or correct this color shift so I can either:
- transform the original image to better predict the printed result, or
- normalize the photographed print so it better matches the original image.
The setup is consistent: I use the same printer and the same camera. Is there an established workflow or literature for this, rather than trying to learn the transformation only from histograms or with a custom ML model?
Originally by Photography Stack Exchange contributor. Source · Licensed CC BY-SA 4.0
Photography Stack Exchange contributor
4y ago
2 Answers
1
The industry standard method would use two transforms encoded as ICC profiles. The input device would use an mapping from its color characteristics to the Profile Connection Space. Likewise the output would use a mapping from the Profile Connection Space to its output color space.
This is the standard because it allowed for connecting any input device to any output device.
Going backwards from the print isn’t really possible in a direct way. You need an input device (such as a camera or scanner) to capture an image of the print. And then you are dependent on its ICC profile (or a logical equivalent) to get back into Profile Connection Space.
That is not to say you can’t do it…that’s what printer profiling hardware does.
Anyway, the way to do the sort of things you want to do is available off the shelf from companies such as Xrite and DataColor using ICC profiles. It is not cheap. But it is as cheap as it gets.
Originally by user95237. Source · Licensed CC BY-SA 4.0
user95237
4y ago
0
Generated from our catalog & community — verify before relying on it.
Yes: the standard approach is color management using ICC profiles, not histogram matching.
In a managed workflow, each device is characterized by a profile:
- the source image/device is mapped into a device-independent Profile Connection Space,
- then the printer/output is mapped from that space into its printable color space.
To evaluate the printed shirt, you must capture it with an input device such as a camera or scanner, and that capture also needs its own color characterization/profile. From there you can compare colors in a common color-managed space.
Trying to recover the original directly from the print photo is not straightforward, because the result depends on both the print process and the camera capture. That’s why profiling hardware and ICC-based workflows exist.
So the practical answer is:
- profile or characterize the camera/input,
- profile the printer/output process,
- compare or convert in a common color-managed space.
A histogram alone is too crude to describe the full color transformation. If you want repeatable prediction, use device profiling rather than relying only on ML or raw histogram comparison.
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