Gerhard Fuernkranz wrote:
(hopefully well-know) gamma to the converted raw images. Therefore if one deals with camera raw images then it may IMO even make sense to assume fixed shapers [preferably gamma 1.0, as captured by the sensor, or otherwise the well-know gamma value which was explicitly applied by the used raw converter] and to estimate the matrix only [Graeme, I guess it should not be too hard to add an option to colprof for prescribing a fixed gamma for the shapers, instead of estimating them along with the matrix].
Hi Gerhard, Pascal and others, of course anything is possible, but I'm not convinced yet of it being desirable. While one can make lots of suppositions about how particular devices work, some experimental proof that the suppositions are correct would be good before changing how things work on the basis of them. One thing to keep in mind is that the profile isn't necessarily meant to mimic the way a device works, it is meant to model the devices overall behavior using the mechanisms the ICC profile makes available. So the per channel curves will likely be different for best match for (say) an XYZ PCS CLUT profile than an L*a*b* PC CLUT profile, etc. The other aspect is that I'm not happy with trying to second guess the modeling. While in particular cased one may be able to make guesses about how one model will better fit than another, having even finer grained options seems to me to be something that most people won't understand or cope with (apart from randomly changing things to see if they work better), and misses what I see as the real issue: Why are the models not as accurate (subjectively) when they are more accurate numerically ? I'd rather fix this so that you don't have to make assumptions about the device or know to set intricate options to get the best possible result ! Some examples of such a situation would help (ie. the .ti3 data plus the colprof params and example images for such a situation).
response to a _fixed_ spectrum at varying intensity). The deviation of a linear sensor from a true linear response is likely smaller than the error of the response curves estimated in this way. Thus simply assuming gamma 1.0 for a sensor which is known to be linear may be more appropriate than trying to estimate its response.]
Right, but another interpretation is that if this is the case, the smoothing is inadequate (too much noise is making its way into the model), and imposing a linear curve forgoes the possibility of modeling any other sources of nonlinearity in the whole system. Graeme Gill.