Marti Maria schrieb:
In the other hand, CLUT based can exhibit discontinuities. Thus, a CLUT based profile can model the weird behavior some devices have. But at a price.Marti,
One should be careful on these profiles, because if they are not smooth, they can fit almost perfectly patches used in training, but be very bad on patches not used. For example, a profile with dE of about 0.5 can give dE > 10 on new patches. These profiles are unusable, despite the good dE.
On more terms in regression, more forced is the gamut to fit outliers, and less smooth is whole profile. So, using 50 terms is good as far as 20 or less terms gives approximately same result.
I agree, especially higher order polynomials, if used for curve fitting, tend to match the provided training set very well, but often give a large deviation for data which not in the training set. Additionally, for noisy measurements (and some noise is basically unavoidable), using too many terms rather fits the noise in the data, instead of actually improving the profile. I've also some doubts, whether any reasonable, accurate extrapolation, based on higher order polynomials, beyond the gamut boundary of the IT8 target is possible at all, is it?
Is it possible, that LCMS probably also uses more terms than necessary or useful?
I'm also wondering, does your new profiler also take noise statistics (i.e. standard deviation) of the measurements into account, e.g. for computing different weights for each patch of the training set, or for estimating the optimal number of terms to use?
Regards, Gerhard
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