The original had varying tristimuli, which in my opinion degrades the superimportance of the spatiotemporal articulation.

This demonstration is absolutely crucial to keep at the front of our lens when we hear the rabble discussing nonsense like “gamut mapping”.

There’s a 10000 thread at GitHub on CSS gamut mapping that is going to screw generations of web developers. https://www.w3.org/TR/css-color-4/#css-gamut-mapping

Folks really need to slow down and think more.

Never mistake motion for action.

So what all of these lines of thought succumb to is the brain wormed idea that “colour is stimuli”. We all know this is false, as the Ekroll and Faul demonstration shows with incredible persuasiveness.

So what **can** we learn from Ekroll and Faul? Is there a deeper pattern here?

I believe there is.

First would be to identify a key factor in the Ekroll and Faul demonstration. In my revision, given that the tristimuli are identical, we can get a sense that the local mean energy will be the +

- sole difference between the articulations. That is, we can expect the neurophysiological inhibitory opponent signals to be of similar gradient maxima and minima.

The key point is the polarity of the signal. What do we mean by “polarity”? This is the idea that there appears to be sizable indications that the information inference computations are grounded strongly by the energy “directions”.

Here, the arrows loosely identify the neurophysiological gradients of our biological assemblies.

All of this is rather interesting if we step back and fully appreciate that in terms of the neurophysiological signals, we are quite literally “hard wired” with energy analysis assemblies. The decrement signals could be broadly considered to be “energy down” gradient signals, and the increment signals are “energy up”.

If we compare to the low probability transparency mode upper right quadrant, we can see that the minima of the RGB uniquely “points” in a different direction.

So how might this related to pictorial depictions that matter more to image authors?

Let’s consider pictorial exposure depictions. Think about what we mentally imagine when we say “increase exposure”. There is a very unique mechanic that few stop to evaluate. Here is a simulated exposure sweep from @barselino, derived from a negative and processed under a custom chain.

This sort of a localised test strip picture is *incredibly* informative if we think about the “energy floor”.

For starters, it is not unreasonable to think of pictorial exposure as “layers of mist” or “layers of tissue” being “stacked” on the pictorial depiction. The key takeaway? The visual cognitive computation of “transparency” is *absolutely key* in our analysis.

And again, think about the “energy floor” as we progress left to right. Remember, RGB, after we remove the encoding transfer characteristic, is *normalized wattage*.

So it is very reasonable to ask ourselves what might happen if we “violate” this “energy floor”. How might we do this? Let’s think about what I call the “purity” for a moment.

What makes an RGB triplet less pure or more pure in terms of tristimuli?

A good number of people know that [1.0 0.0 0.0] and [0.0001 0.0 0.0] are identical colourimetric purity; the “distance” from the achromatic global centroid of R=G=B is the same.

So what here is “distance” if we reframe it as “energy”?

I have begun to believe that we can think about pictorial depictions as “fields”. That is, instead of “pixels”, it is useful to think of a given assembly as a series of variable “densities”. Think of it as a mip map representation for rendering wonks, or a series of progressively “blurry” versions of the same picture.

What is interesting here is that we can consider max(RGB) and min(RGB) of a given field, as an “energy window”. I’ve been riffing on the idea of an “energy cage”.

We know that our ganglion cells “pool” varying “field” dimensions of the bipolar cells. Varying the spatial “density” is a reasonable way to think about the “energy fields”.

It goes without saying that folks just look at this localised test strip picture and see “rectangles” of “exposure”. But how are we achieving this? This is a key question. What is governing the depictions of “a series of rectangular strips”?