(edit, I was told a new user can only post a single image, so I’ve provided links to what used to be images)
A truce it is! Thanks for addressing that. Sorry again for being so snippy above.
Your talk of Rec. 2020 got me looking into that. I hadn’t heard of it before (perhaps not surprising since I don’t even know what “Blender” or “Cycles” is, so I’m really a fish out of water here). But once I saw the colorimetric specs on it, it was easy to modify my optimization for it. So I’m finding the least slope spectral curve with magnitude 0 to 1 that has a given Rec. 2020 linear rgb value. That’s all the “human” input that is going in.
For Rec. 2020 rgb = (1,1,1), I get a recovered spectrum of all 1’s, as expected:
For intermediate values, like rgb=(0.3,0.5,0.8) I get nice smooth curves:
Now this is where it gets pretty cool. Going for the extremely chromatic color, rgb=(0,0.15,0) I get
Even though the optimizer is trying to find a smooth curve, the only option it has is to spike at the G primary!
I tried larger chromatic values like (0,1,0), but the optimizer says no dice. Not gonna happen. Still wrapping my head around that.
The way I look at entropy is how much “human” there is in the algorithm, or as you say, how much information is presupposed. But your suggestion to “incorporate some kind of peaking/diffusion parameter” is just that very type of presupposition! I’m going to try other types of objective functions, like simply minimizing the area under the reflectance curve, and see what happens.
I’m worried that all my talk of spectral recovery is getting off-topic in this thread. Any suggestions on other places where this discussion would be more on-topic?