Difference between revisions of "Creating Bluish Noise With Minimal Compute"
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if ABS(B-C) > ABS(A-C) then A=B | if ABS(B-C) > ABS(A-C) then A=B | ||
C=(C+A)/2 ; exponentially decaying running average | C=(C+A)/2 ; exponentially decaying running average | ||
| − | ; | + | ; "A" is holding your next bluish noise value |
The algorithm creates the expected +3 dB per octave power increase, and works equally well with LFSR noise sources. | The algorithm creates the expected +3 dB per octave power increase, and works equally well with LFSR noise sources. | ||
Revision as of 00:18, 23 May 2024
Blue noise is a type of noise with a power density that increases with frequency, giving it a high-pitched, hissy sound. Visually, it manifests as a pattern with minimal low-frequency components and a relatively even distribution of points, and is frequently used for dithering. Blue noise is often used for its aesthetically pleasing randomness, since numbers don't clump together as they would with truly random (white noise) sources.
The Algorithm
Blue noise is typically created by generating random values, and massaging them in various ways to not be as clumpy. This isn't a great approach for older platforms that don't have a lot of compute or ram.
An alternative approach is to use an exponentially decaying average, to ensure the next new number isn't clumped together with recently returned numbers...
A=RAND B=RAND if ABS(B-C) > ABS(A-C) then A=B C=(C+A)/2 ; exponentially decaying running average ; "A" is holding your next bluish noise value
The algorithm creates the expected +3 dB per octave power increase, and works equally well with LFSR noise sources.
Adding more random values into the comparison will purify the blue noise, at the expense of addition compute time.
Authorship
The novel Bluish Noise technique described here was created by Mike Saarna.
