Random numbers can be useful in graphics and game development, but they have a pesky and sometimes undesirable habit of clumping together. This is a problem in path tracing and monte carlo integrat…
That’s a very long list of different techniques with examples, very cool!
Though I wonder, is there some connection to image processing, high dynamic range?
Or audio compression, the kind which brings out the kick in the mix, not the kind which saves disk space?
The similarity I see between all three fields is, they try to bring down extreme values, outliers, to level the field, while still retaining characteristics.
I didn’t know about that [under this name], so thanks for bringing it up. But no, I meant something slightly different.
Colors of noise describes how to generate different distributions. What I meant was how to transform distributions.
Many of the examples in the article start with a random number distribution, and then transform it to reduce discrepancy.
This reminded me of audio/video signal processing. For example, one can take a picture and transform it to reduce discrepancy (so that neither very bright parts nor very dark parts overshoot). Or you can take an audio sample and transform it to reduce discrepancy in loudness.
So the idea was that maybe techniques of either field (RNG, audio, video) could be applied to both other fields.
That’s a very long list of different techniques with examples, very cool!
Though I wonder, is there some connection to image processing, high dynamic range?
Or audio compression, the kind which brings out the kick in the mix, not the kind which saves disk space?
The similarity I see between all three fields is, they try to bring down extreme values, outliers, to level the field, while still retaining characteristics.
I’m guessing you’re referring to the colors of noise?
I didn’t know about that [under this name], so thanks for bringing it up. But no, I meant something slightly different.
Colors of noise describes how to generate different distributions. What I meant was how to transform distributions.
Many of the examples in the article start with a random number distribution, and then transform it to reduce discrepancy.
This reminded me of audio/video signal processing. For example, one can take a picture and transform it to reduce discrepancy (so that neither very bright parts nor very dark parts overshoot). Or you can take an audio sample and transform it to reduce discrepancy in loudness.
So the idea was that maybe techniques of either field (RNG, audio, video) could be applied to both other fields.