No, diameter means the width of the filter in pixels. Each side would extend 1.1 pixels for a 2.2 diameter filter.

If you compare filters at the same width, gauss will be softer, which is why you can lower the diameter.

A Gaussian filter has infinite support, and is characterized by its variance (or standard deviation). https://en.wikipedia.org/wiki/Gaussian_filter

A ‘Gaussian’ which extends 1.1 units is a non-standard and not clearly defined thing. Are you picking some fraction of 1.1 as the standard deviation and truncating after? (Often Gaussian filters are truncated after 3 standard deviations, or similar.) Are you multiplying by some other window function? ...

Can you link to a more explicit formal description of what you mean?

When I say 2.2 pixels as the diameter, I mean roughly that a pixel will be made up of all the samples 1.1 pixels or closer to the center of the pixel and they will be weighted by a gauss curve. How that gauss curve is actually used is not formal or strictly defined, but is usually a curve that goes out to about 3 standard deviation a baked into a LUT and normalized.

A curve that uses more standard deviations would have to be wider in pixels to look similar.

Do you have some code for what you specifically are talking about? Or a formal description?

You have been saying here “this kernel is better than all of the alternatives” but it’s hard to evaluate that kind of claim without knowing precisely what you mean.

“not formal or strictly defined” is not super encouraging.

I never said it was better than all alternatives, I said my experience is that it is difficult to beat in a general case.

Bake a gauss curve out to three standard deviations into a LUT and normalize it. You can look at what PBRT does.

Keep in mind that I was replying to someone confused by all the choice of filters and was giving him a very solid starting point. This isn't some grandiose claim of scientific exploration, it's experience.