[esens]

Has someone come up with a mapping from Perlin to Simplex? It looks to me like Perlin is slightly low resolution and less amplitude scale. Thus it should be possible to write an equation that mostly maps one to the other -- thus simplifying replacing Perlin with Simplex and vice versa while keeping the distribution relatively the same.

With statistics I bet one could derive this mapping equation pretty quickly. Then it would be interesting to compare the resulting residuals or statistical differences at that point. Because it would be comparing the actually differences rather than these more surface differences.

[noduerme]

I'm not sure that would be possible. These are both just ways of problem-solving the gradient of diffusion of random bit noise once it's blown up to a larger size. The Simplex method appears a lot more prone to volatility. The peaks and valleys are more extreme given the same parameters. Crushing the peaks and valleys would lead to compression artifacts or else lengthening the frequency to stretch out the band. Maybe I'm wrong, but fundamentally the fastest way would be to reproduce the underlying noise and then re-render it in the other method.

[SamBam]

If we have two different random number generators, and yours has various artifacts that make it non-random in some circumstances (and perhaps mine has different artifacts that make it non-random in different circumstances) then there isn't just some simple "mapping" you can do to go from yours to mine.

[esens]

I was trying to say one could map the std deviation and mean feature size between the two. Currently that isn't done. Thus the 2D scale appears to differ between the two as well as the min/max delta.