Interesting… how did you define the tone curve for lumariver/dcamprof?
I downloaded the JPEG, it definitely re-energizes the S-curve. You got me thinking to do something similar with ACES2065-1, although that is still scene-referred…
how would you go about this on a given image?
I saw that …came across this as well…given your work with spectra might be of some use to you down the line…GitHub - ampas/rawtoaces: RAW to ACES Utility
rawtoaces is where I got my monochromator-measured D7000 SSF dataset. I’m exchanging emails with them about their licensing; if that works out I’ll add their cameras to GitHub - butcherg/ssf-data: Spectral sensitivity data for digital cameras
Nice…
Could the standard 3x3 matrix be transformed in the same way? And if so, Would it help solve its saturated fringing/gradient issues?
We consider the physical world of image capture to work linearly so ideally we would like our processing to also work linearly. That would be possible if certain conditions were met in practice that however typically are not. For instance CFA spectral sensitivity functions would ideally be linearly related to cone fundamentals in the retina. If they were all we would need is a 3x3 matrix to bop around the needed colorimetric spaces.
Unfortunately that’s not the case: sensor SSFs are just an approximation to the ideal, the problem is overdetermined, there is an infinite number of possibly ‘correct’ matrices, if we want just one the best we can do is come up with a Compromise Color Matrix that minimizes potential errors according to some criteria. Think of it as fitting a curve (or a plane or a solid) to a lot of noisy dots. We sometimes try to compensate for some of the biggest errors via a look up table, but even that is just another form of curve fitting that only really works for the finite number of tones that were corrected manually.
So no, the standard 3x3 matrix cannot be made to work perfectly every time, it can only be made to work ok most of the time. All solutions that look more pleasing are just that, more pleasing. This one time, or perhaps in these types of conditions.
Jack
PS Some additional thoughts on this here.
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Thanks for the link, I’ve translated the arri log tone curve EI800 to the g’mic parser and used it in photoflow before the conversion to the working space rec.2020 linear, I’ve then used only levels and curves for tone manipulation, no saturation or channel mixer module.
(x > cut) ? c * log10(a * x + b) + d: e * x + f
-fill i=i/255;if(i>0.010591,i=0.247190*(log(5.555556*i+0.052272)/log(10))+0.385537,i=5.367655*i+0.092809);i*255
DSC08363.pfi (35.8 KB)
However the log unbreak profile in darktable works great too
DSC08363.ARW.xmp (8.4 KB)
@age Hey thanks for sharing that I never really got how to use the unbreak profile…clearly you do…I guess I need to get up to speed…just need some time to read…
I took a look in your xmp…what is the curve that you used in the rgb curve module??
It restore and add contrast together with the levels tool
It looked a lot like a basic ACR tone curve in shape…I was wondering if you created it by math or by dragging it visually…that was more my question….
Still not sure how to use the unbreak profile but it seems powerful esp with underexposed images it seems to give you a better starting point……
I’m also interested in acheiving this, been trying with lutcalc and dcamprof but my image it’s sometimes overexposed (after converting it back to rec709 with a lut or with color space transform in resolve), should i try to match middle gray from both curves at 18% compensating with negative exposure (-2.4ev on linear to EI800 LogC) ? can you share your dcp?
Hi @comadrejo, and welcome!
… sometimes overexposed (after converting it back to rec709 with a lut or with color space transform in resolve)
Isn’t that overexposure visible already in resolve? What does the vectorscope show? Wouldn’t it be easier to fix this problem in resolve?
Have fun!
Claes in Lund, Sweden
If it is overexposed after converting to rec709 in Resolve, then try using tonemapping in the colorspace transform.
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Thank you for the warm welcome my friend!