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Blue noise is broadly useful in imaging, you can use it for high quality dithering, or for perceptually smoother sampling in raytracers for example. Here you c
by jsheard 2y ago
Blue noise is broadly useful in imaging, you can use it for high quality dithering, or for perceptually smoother sampling in raytracers for example.
Here you can see the same rendering using white noise (true randomness) or blue noise, with the same number of samples:
https://www.shadertoy.com/view/3sfBWs https://www.shadertoy.com/view/3sfBWs / https://i.imgur.com/uDybnPm.png https://i.imgur.com/uDybnPm.png
- Art9681 2y agoThank you. Now I understand the use case.
- majoe 2y agoNot only in imaging. More generally, blue noise is often used with all kind of Monte Carlo simulations, since the more evenly spaced sampling can speed up convergence. Raytracing can be seen as one such MC simulation
- buildartefact 2y agoIn path tracing blue noise does not increase convergence, but does reduce the perceptual error.
- majoe 2y agoThe upper bound of the approximation error of random Monte Carlo methods decreases with O(1/sqrt(N)), whereas the error for quasirandom Monte Carlo methods (omitting some details) decreases with O(1/N) (where N is the number of samples) [0]. I don't think path tracing is an exception here. I would even argue the opposite: For the same variance I would expect the error to be more perceptible for (blue noise) quasirandom MC, because it can lead to regular patterns in the noise. [0]: https://en.m.wikipedia.org/wiki/Quasi-Monte_Carlo_method https://en.m.wikipedia.org/wiki/Quasi-Monte_Carlo_method
- deleted 2y ago[deleted]