[argyllcms] Re: colprof with 836 data points
- From: <graxx@xxxxxxxxxxxx>
- To: <argyllcms@xxxxxxxxxxxxx>
- Date: Mon, 3 Feb 2020 19:04:18 -0500
Gerard,
Your suggestion is well taken! Thank you.
I was thinking about using random numbers?
Red = Randomize() ' Number between 0.0 and 1.0
Green = Randomize()
Blue = Randomize()
But that would only test the Device to PCS direction.
Still, that would be a start.
/ Roger
From: argyllcms-bounce@xxxxxxxxxxxxx <argyllcms-bounce@xxxxxxxxxxxxx> On
Behalf Of Gerhard Fuernkranz
Sent: Monday, February 3, 2020 6:03 PM
To: argyllcms@xxxxxxxxxxxxx
Subject: [argyllcms] Re: colprof with 836 data points
Am 03.02.20 um 21:13 schrieb graxx@xxxxxxxxxxxx <mailto:graxx@xxxxxxxxxxxx>
:
Decided to "Go for the gold!" and got 836 data points for my lowly NEC
PA271W monitor.
Here are the important statistics:
peak err = 1.146645, avg err = 0.277301, RMS = 0.314956
For 300 data points, the statistics were:
peak err = 0.888202, avg err = 0.244000, RMS = 0.285845
As Gerard pointed out, the statistics may have gone up but the "splines"
have more "real data" to "chew on" instead of leaving whole swaf of color
space uncharacterized.
Since you already have the readings now, the 836 points might be a good test
set for checking the 300-point profile (and also vice versa, if the points
in the two sets are independent, i.e. if the 300 points are not just a
subset of the 836 ones).
What is the meaning of "Overdetermination" in the context of fitting
splines?
I mean a system with more equations than unknowns (or in the context of data
fitting: more data points than the number of model parameters which need to
be estimated).
[ But agreed, I'm not sure if I should use this term also in the context of
a non-parametric model, which is per se significantly _underdetermined_ (due
to a huge number of parameters), and which gets only constrained via
regularization (smoothing) to an _effective_ number of parameters lower than
the number of supplied data points. ]
Regards,
Gerhard
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