[argyllcms] Re: Determining proper error value for -r
- From: Ben Goren <ben@xxxxxxxxxxxxxxxx>
- To: argyllcms@xxxxxxxxxxxxx
- Date: Tue, 23 May 2006 12:11:31 -0700
On 2006 May 23, at 8:11 AM, Graeme Gill wrote:
> The most sensitive "real world" test I stumbled across, was to
> simply make up the profile, and eyeball the gamut surface. I
> found quite noticeable changes in the smoothness of the gamut
> surface, as I varied the -r factor in profile. Too small, and
> the surface was noticeably bumpy. As the number increased, the
> surface got visibly smoother, and looked more like one would
> expect for a well behaved device. The self fit errors rise as
> the -r factor goes up too, so I stopped at a suitable "knee"
> point.
I generated 20 profiles of a 960-patch chart of plain paper, all
identical except for the -r value; that, I varied in 0.1-step
increments from 0.0 to 1.9.
Looking at the plot in Apple's ColorSync Utility, there's very
little difference from -r 0.0 to -r 0.5. At -r 0.6, it visibly
gets smoother but retains the same shape. From that point until -r
1.3, the overall shape starts to soften or mush. There's not much
difference between -r 1.3 and -r 1.9.
I just made test prints of -r 0.0, -r 0.6, -r 1.3, and -r 1.9. Of
the four, -r 0.6 is the best. -r 0.0 has some artifacts in
a Grainger rainbow, and midtone neutral steps are slightly
warm. In -r 1.3 and -r 1.9, the highlight neutral steps are
slightly purplish. Neither -r 0.6 nor -r 1.3 show artifacts in
a Grainger rainbow, but -r 0.6 is a bit smoother and more
regularly-shaped. In -r 1.9, some (different) artifacts start to
appear. The entire gray strip of -r 0.6 is the most neutral of the
lot. Fine detail in a black-and-white photo is best in -r 0.6, but
some shadow details are /slightly/ better in -r 1.3.
(I should note that, before I got the i1 and started using Argyll,
I'd have been absolutely thrilled to get even close to the worst
of the lot.)
The spreadsheet I created from the reading of the 39-patch chart
duplicated eight times came up with the following:
+----------+-------------+-------+-----------------+
| Data | Avg Std Dev | Scale | Std Dev / Scale |
+----------+-------------+-------+-----------------+
| L | 0.35 | 101 | 0.35% |
| a | 0.56 | 256 | 0.22% |
| b | 0.43 | 256 | 0.17% |
| spectral | 0.43 | 128 | 0.33% |
+----------+-------------+-------+-----------------+
(A bit of explanation: I individually calculated the standard
deviation for the eight copies of each patch--the standard
deviation of the L values for all eight white patches, the
standard deviation of all the spectral values for the eight
purplish blue patches, and so on--and then took the average of all
those standard deviations. My statistics isn't good enough to know
if I just made a major boo-boo or not, though....)
The only thing I don't quite understand yet is how the value used
for -r translates into an actual percentage for an error. Does -r
0.6 mean that the actual percentage error is 0.3% or 1.2%? I'm
confused....
Anyway, if it's the former, I think this technique shows the
potential for a good deal of merit. If the latter, I'm clearly all
wet....
Cheers,
b&
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