[argyllcms] Re: Optimal smoothing factors
- From: Graeme Gill <graeme@xxxxxxxxxxxxx>
- To: argyllcms@xxxxxxxxxxxxx
- Date: Mon, 20 Feb 2006 12:01:18 +1100
Gerhard Fuernkranz wrote:
Gerhard, thanks for sharing your results.
I had not done the same exercise in XYZ space, but I'm afraid, the errors are
heteroscedastic in
XYZ as well. I would really be surprised if the repeatability error of a
printer would be
homoscedastic in XYZ space.
Yes, I'd imagine that the errors introduced by the printer won't
be so well correlated to linear light, although I would expect the
errors from the instrument to be. As a first order approximation,
I would guess that the printer would generate a normally distributed
error in the amount of ink it lays down, and then a (rough) model of
ink level to color would translate the statistics into color terms.
I'm guessing that instrument errors will be most significant at
the dark end, where slight errors in light level are magnified
by their ratio to 0, and that printer errors might be most significant
at the light end, where slight errors in the ink level, are
magnified by their ratio to white.
since your rspl weights are weights for the suared error. As you do not support
separate weights
for each RSPL output dimension, but only a single weight per point, a
reasonable compromise is
likely to use 1/(variance_L+variance_a+variance_b) as point weight.
Actually, I think the latest code does have support (at the fundamental
level)
for different smoothness weights for each (input) dimension, and it would
be easy to add different weights for the output dimensions, since each
output channel is fitted independently (in fact, I was tempted to do this
when I was doing the tuning, since the simulations indicated that the optimal
L* value smoothness is different to the a* and b*, which is hardly a surprise.)
Given I was struggling to tame the complexities of determining good smoothing
factors for combinations of different dimensions, number of sample points, and
uncertainty level, I decided not to further complicate things at that time.
Graeme Gill.
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