I used to judge the quality of the A2B table of a profile, by its correspondance with the original sample data;
This is understandable, but not the correct measure of the A2B table quality. The correct measure is the error to the actual device behaviour. Of course, the original sample data may be the only available information related to the actual device behaviour, hence the common and understandable use of this to judge A2B quality.
The approach to tuning the algorithms in Argylls profiling code that I took, was to create a high quality (lots of sample points, say 10000) profile from a real device, and then treat that profile as a perfectly characterized master test device. I take that profile, sample it with a smaller, more realistic test set (ie. 500-3000 sample points), generate a sample profile from that, and then compare the errors with 10000 or more sample points back to the master test device profile. The settings that minimize this error, are often not those that minimize the error of the realistic test set to the sample profile. Now maybe this indicates limitations of the algorithms being used, or maybe this indicates that some degree of smoothing actually benefits the actual profile accuracy.
sometimes the sample data was exactly the gridpoints (evenly spaced target), why wouldn't the A2B table be just a dump of such data?
In that situation I would generally agree, but even so, it could turn out that there is a better fit to the underlying device characteristic, if the data is smoothed slightly in some way.
It's unlikely in real world situations that one would measure the whole grid this way. The only situation I know of this happening is in characterizing movie film, where creating and measuring 35937 patches automatically is actually feasible (ie. that's about 25 minutes worth of film).
I understand 'inaccuracy in the measurements"; and supposedly making 4 or more measurements of the same target, then doing an intelligent average (discarding stray samples), should get rid of such inaccuracy. What do you mean by "sampling noise"?
You could do that, but would you be better off using those extra 3 measurements to sample other parts of the device space ? (ie. stratify your sample in device space, as well as repetition space). That's certainly what I'd tend to do, the "averaging" becoming the "smoothing".
By "sampling noise", I was trying to convey the idea of there being noise due to the sampled nature of the information determining the interpolated response, something analogous to quantisation noise in other sampling situations. In a linear interpolation of a set of sample points (for instance using Delaunay), this is evident in the flat/inflextion/flat/inflection nature of the model. Other models (such as inverse distance weighted) have a different characteristic, but (without going to a very great deal of trouble), the sparse nature of the original samples will tend to show through (for instance, using the simplest form of inverse distance weighting, the "slope" of the interpolated "surface" will be zero at each sampling point.) By fitting a higher resolution grid to the sample data, there is the opportunity of having smoother transitions between the sample points, disguising their presence in the overall characteristic.
For the "typical device behaviour" part I'm a little more skeptic. If building an A2B of an offset press or a laminate proof, there's some smoothness to be expected -because they're analog processes- and therefore the samples should exhibit reasonable behaviour, and you should correct them if they don't (like PrintOpen's "automatically correct measurements").
There is a whole range of stuff one can do here, right down to very detailed models of a particular process, that will greatly improve the interpolation, and reduce the number of samples needed, but I was mainly focused on the general case (which is what Argyll is set up for), where there is no assumption made about the particular process involved. Even at this level, it is likely to be a more reasonable assumption that the device behaviour through and between sample points is smooth, rather than discrete. It is hard to make models that are based on the original sample data behave smoothly and reasonable, whereas it is easier to make a higher resolution regular sample grid behave in a sensible way, adhering to a uniform smoothness metric, as well as allowing smoothness to be traded off against sample point interpolation accuracy.
But nowadays, what can you call typical in inkjets? with ink and paper technology ever changing, ultrachrome inks, gloss differential, swellable papers... CMYKRGB printers, variable drops, etc etc... there's very little that can be assumed about them, and smoothness might not be for granted. So doesn't it make more sense to just leave the data as-is, maybe after averaging some prints and/or measurements?
But there is no such thing as "as is" in interpolation. You have to interpolate, and you have to assume some sort of model for the interpolation.