I want to compare color histograms between a digital photo and its physically printed in cloth (like a T-shirt) version. The process is simple:
- Printing a digital image (with good resolution) in a T-shirt
- Take a picture of that printed T-shirt in proper alignment and crop
- Compare histograms
As one might expect, the color histogram of the photo of the printed T-shirt is very different from the original image, as it should be. What I want to know is if there is any known method to achieve one of the following:
- From original digital image, transform colors so to be closer to printed colorspace histogram.
- From printed T-shirt photo, try to normalize or transform in some way to achieve back original colors.
I am aware that the color distribution of the printed T-shirt depend also on the camera that captures it. Right now I am thinking of training a ML model to try to learn the color transformation between two colors distributions (original and physically printed).
However, I wonder if there is some literature about that or some procedure I can perform using my camera to try to find that color transformation so I can apply to other prints.
My goal is to generalize (predict) the printed color distribution transformation from original digital image. As I'm always using the same printer for print, and the same camera for the photo of the T-shirt, I guess this should be feasible.
Thanks in advance.