[argyllcms] Re: Black turning down problem (the -r trick)
- From: Gerhard Fuernkranz <nospam456@xxxxxx>
- To: argyllcms@xxxxxxxxxxxxx
- Date: Sat, 15 Jan 2011 03:15:53 +0100
Am 14.01.2011 20:03, schrieb Nikolay Pokhilchenko:
> Graeme, I see an analogy with audio analog-to-digital conversion
There is indeed such an alalogy, but...
> (2D quantization - time and amplitude). May be the profiler must
> automatically limit the harmonic spectrum of device response bumps when the
> bumps spectrum is wider than can "digitize" the A2B. There should be the
> Nyquist-Shannon sampling theorem taken in account. I suggest to perform low
> pass filtration (LPF) of device response data before computing of A2B table.
...when the A2B table is created, it is already too late to apply an
anti-alias filter. When we reach this stage, the device response _has
already_ been sampled (at the points corresponding to the the patches on
the target). The sampling of the device response basically happens when
you print (and then measure) the target, and not when the A2B table is
afterwards created from the samples.
But a Nyquist filter must be applied _BEFORE_ sampling. This means you
would need to install the anti-aliasing low-pass filter _in the device_
when you print the target, so that the printed target already reflects
the sampling of the low-pass-filtered device behavior. But if you had
any opportunity to make the device response smoother, then you would not
have to care any more about high-frequency bumps, because they would
disappear then anyway. So we rather have to consider the case where the
device is as is, and you can't change its behavior. In this case you
can't apply any anti-aliasing filter and the consequence of the sampling
theorem is that you can only sample at a high enough frequency, greater
than twice the highest frequency of the bumps in the device response,
which simply means that you need to use enough patches. Sampling at
higher and higher frequencies may become impracticable though, as CMYK
space is 4-dimensional, i.e. doubling the sampling frequency requires to
print and measure 16 times as many patches!
Creating the A2B table from the measured patches does not involve any
sampling, but spline fitting. The sampling theorem does not play a role
here. What here happens is is actually the opposite of sampling, i.e.
given a set of discrete samples we try to find a smooth, continuous
function which approximates the original device behavior. Fitting
smoothing splines to scattered data does an implied smoothing anyway, so
I don't see any need for a separate smoothing pass before fitting the
splines to the data.
Regards,
Gerhard
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