After playing around a bit with all of these different variations, I decided that I did not like the behavior of Toe constrained by the grey intersection point. Changing contrast when adjusting toe doesn’t make sense.
I settled on using the piecewise hyperbolic with “decoupled toe and surround”. I played around a bit and roughed in a few presets for
The behavior and appearance is nice but I don’t like how complex it still is.
I keep going back to @daniele’s original formula, and thought I would give the same exercise a try with his original “minimally constrained” formulation and see how it went. It was actually surprisingly easy to dial in the same behavior, even without being able to specify the precise intersection points. (I did make one modification in order to get the output domain scaled to the right level for PQ output). Tonemap_PiecewiseHyperbolic_Presets.nk (4.0 KB)
Edit: Woops! I realized looking back at this that I uploaded the wrong nuke script here. Here’s the correct one which contains Daniele’s compression function, as described above. Tonemap_Siragusano_Presets.nk (2.5 KB)
Which leads me to an observation that I probably should have had before spending a few weeks on the above math exercise: In a scenario where we are building something that has preset parameters for different displays and viewing conditions, is a high level of complexity in the model justified in order to achieve precision of intersection points? What level of precision is necessary here and why?
I realized I may not have shared the super simple EOTF / InverseEOTF setup I’ve been using, which may be a necessary counterpart for testing the above tonescale setups, especially with HDR: ElectroOpticalTransferFunction.nk (5.2 KB)
That’s a very good point, @jedsmith. I don’t believe we are trying to build a fully dynamic, user controllable display transform. So as long as we can find values that create the desired intersections for a set of common targets, and clearly document a methodology for calculating them for other targets, it is probably not necessary to restrict ourselves to an algorithm where the intersections can be solved “on the fly”.
I’m testing your latest tonemaps on some images and wanted to make sure I had things set up correctly. I’m on a standard sRGB monitor, I have the image set to ACES-2065, the Nuke view transform set to Raw, and am using the Display EOTF node from one of your scripts, set to sRGB. Here’s a screenshot of Nuke:
As you can see, this seems to appear somewhat desaturated. On a hunch I added in a ACEScg to sRGB color matrix from one of your earlier Nuke scripts (20190119_tonescales.nk). Here’s a screenshot of Nuke:
Hey @Derek !
Thanks for getting your hands dirty. We need more people testing things out!
The stuff I’m posting in this thread is purely related to the “tonescale” component of the display transform. While important, it is only one component. A potentially incomplete list of different modules we might need:
Input Conversion - Convert input gamut into the rendering colorspace, in which we will “render” the image from scene-referred to display-referred. The rendering colorspace might be an RGB space or an LMS space, or something else entirely.
Gamut Mapping - We may want some handling of chromaticities which lie outside of our target output gamut.
Whitepoint - We may want to creatively change the whitepoint of our output in order to create warmer or cooler colors in our output image regardless of what display device whitepoint we are outputting to.
Rendering Transform - There are many valid approaches for this. ACES uses a per-channel approach where the tonescale curve is applied directly to the RGB input channels, as you were doing in your above experiments. Since I’m working on a chromaticity-preserving display rendering transform, I’ll outline what might go into that.
Separate color and grey using some norm.
Apply some tonescale to grey to convert from scene-referred to display-referred.
Apply some model for surround compensation and flare copmensation.
Apply some chroma compression to highlights so that very bright saturated colors are nudged towards the achromatic axis as their luminance increases towards the top of the display gamut volume.
Display Gamut Conversion - Convert from our rendering colorspace into our display gamut. If you’re looking for a tool do do arbitrary gamut and whitepoint conversions in Nuke you could check out GamutConvert.nk.
InverseEOTF -We need to apply the inverse of the Electro-Optical Transfer Function that the display will apply to the image before emitting light from your display.
Clamp - And lastly, maybe we want to clamp the output into a 0-1 range, to simulate what is going to happen when we write the image out into an integer data format.
The “DisplayEOTF” gizmo is pretty old technology. It is basically just the Inverse EOTF guts of the Nuke ACES Output Transform implementation I did, ripped out and put in it’s own node. I would recommend that you use the InverseEOTF node in the ElectroOpticalTransferFunction.nk file I posted above instead. It’s a lot simpler.
If you’re using a stock ACES config in Nuke, and your read node’s input colorspace is set to ACES - ACES2065-1 as I see in your screenshot, this is doing a gamut conversion from AP0 to AP1. This makes your “Rendering colorspaces” ACEScg. This is the same as the ACES Output Transforms. The ACEScg → Rec709 matrix is essentially your display gamut conversion. and your DisplayEOTF node is your Inverse EOTF curve.
Hope this big wall of text helps clarify things, or at least brings some new questions that will help in your journey of learning.
While the Hyperbolic is not maintaining the colors (the 12 colors become 6), what it is doing is all going to white with increased exposure (compare the last row of both images). The OpenDRT is not going to white at 20 stops. In fact it looks the same at 100 stops as it does at 20. So it appears to never go to white. Perhaps that’s an artifact of this being a CG render, and the same would not happen in a photo? I’ll need to do further tests there…
First, it looks like there’s an error in the setups you picture: The input gamut in the OpenDRT node should be ACEScg not ACES. This error is causing the output to be horribly over-saturated. Maybe you want to edit your post and update the images?
This is a valid creative preference. However, I don’t believe the display rendering transform is the correct place to apply creative preference. If the scene looked “tangerine orange” with your eyes, and the scene looks the same when displayed on a monitor, the display rendering is doing its job. My goal is a faithful representation of the chromaticities represented by the input image. It’s not to say that creative adjustments can not happen, they would just not be part of the display transform. I believe this approach would actually allow more creative freedom because it would allow for varying creative preferences without altering the display rendering transform.
Of course the missing piece here is the LMT, which would apply some sort of creative look. I know it’s difficult to imagine without being able to see something working.
Hope that makes sense, and I hope you’ll update those images with the proper gamut conversion. Curious to see what your comparisons look like properly rendered.
I appreciate the intent and importance of your question. I’m coming at this from the perspective of an artist, and so if you are looking to have these terms defined in scientific terms (again a totally valid aim), I’m afraid I won’t be much help there. I can say what it means to me as an artist if that would be helpful.
So we do not get stuck on display at full emission R, G or B ? Because it reminds us of films ? Or maybe because it is the only way to respect the RGB ratios (or creative/scene intent) at display ? So they don’t get distorted ?
It is also called “bleaching” or “highlights desaturation” ? It is the path that a certain chromaticity would follow towards the achromatic axis as exposure increases ?
Oh my god, I’m terrible at this ! I answered your questions by more questions.
Yes, totally. My intent/desire is similar in that I want as an artist to be able to get the colors I want on my “canvas” and want my tools to help me to do that.
Let me also add that what I am posting should not be taken as a verdict or conclusion at all. Rather I’m hoping that as I share what I’m observing as an artist, you can help me parse and understand that from a scientific perspective in order to get the bottom of what is going on. I find that kind of collaboration really enlightening, and I’m grateful to be able to have that conversation here.
At this point, I feel that I need to dig deeper into the two things I’m seeing. I’ll post tests.