[argyllcms] Re: Best (smallest) profiling patch set
- From: Graeme Gill <graeme@xxxxxxxxxxxxx>
- To: argyllcms@xxxxxxxxxxxxx
- Date: Mon, 24 Apr 2006 15:58:35 +1000
Gerhard Fuernkranz wrote:
So I've the impression that at large noise levels with 50 data points,
the MPP model behaves worse than the non-parametric model (with optimal
smoothing factor), particularly the max. error of MPP is pretty large
(31 dE!), although one would probably expect, that a parametric model
would outperform a non-parametric model at higher noise levels. But it
looks like the MPP model has still a pretty large number of parameters,
and therefore likely still requires more samples to become more immune
against noise.
Interesting observations. The MPP fitter hasn't really been
tuned up in regard to its behaviour with noisy readings.
Probably the place to start, would be in increasing the
four #defines TRANS_BASE, TRANS_HBASE, SHAPE_PMW and COMB_PMW,
and/or altering the weighting scheme that TRANS_BASE and TRANS_HBASE
participate in (xicc/mpp.c)
In theory the MPP model is better when some fundamental values
are missing, such as only having the primary colorant values,
and white. It will compute approximate secondary overlap colors,
and use them as anchors for the resulting profile. The rspl behaviour
under such circumstances might not be very realistic.
For noise-free data, an astonishingly good profile could be generated
from only 50 patches, for a device which behaves like
USWebCoatedSWOP.icc. But if the data are "noisy" (measurement errors,
reproduction error of the device, etc.), then the difference between a
profile created from a small number of data points and a large number
becomes clearly evident.
So currently for 50 patches, the normal profile procedure might be as good
as mppprof, if not better.
Graeme Gill.
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