Sorry for a sort-of long question:  I’m curious about using some 3-band measurements (as with a particular RGB camera) to estimate the response in a different band (call it X, assumed to be contained within the 400-700u limits).  I think I understand that linear regression-type models often give good results – that for reasonable (i.e. mostly smooth) illumination and reflectance spectra, the response in some new band X is often well-represented with a combination of the available RGB responses as a regression calculation.

Your documentation also mentions the many-to-one mapping between incident spectral irradiance shapes, and RGB triplets.  This essentially recognizes that the variety or space of “natural” spectral shapes is larger than can be represented with 3 values (RGB or any other 3 values).

My question is if you have some sense of “how bad” the cone-catch (regression) calculation could be for some unknown incident spectrum.  I’m not thinking of a pathological, unnatural incident spectral shape – but maybe one that is not from the types of materials used to train the regression models for cone-catch.  That is, if I measure some RGB, and perform the cone-catch calculation for the X-band, how bad could the result be (even if it is good for most spectral shapes)?

It seems that there may be some (small) fraction of the natural spectral shapes not well-represented by the regression equation.

Since the incident spectrum is unknown (we have just RGB values) do you know of a simple way to estimate or enumerate the distribution of X-band responses over the different incident spectral shapes that all produce the same RGB response?  I guess this would depend on the band X, and also on the RGB values?

Is there some way to enumerate the possible spectral shapes (sampled, as in your database, say) that all produce the same RGB values (for some particular RGB)?

Sorry if you have explained this somewhere and I overlooked it.

How “bad” can a cone-catch calculation be?
jolyon Answered question July 10, 2020