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The algorithm described in Extreme Learning is incorrect: it does not generate blue noise.
Blue Noise is uniform in high frequency with no low frequency component. While the blog post claims that we need not sample the annulus uniformly, the post provides no justification for this claim. By sampling non-uniformly, the algorithm preferentially generates samples that are (r + epsilon) units apart at the expense of the interval (r + epsilon,2r). This is trading correctness for speed.
The text was updated successfully, but these errors were encountered:
Yes you are right. Thanks for raising this. This library was created for the purposes of game development and the dramatic speedup that comes with the loss of correctness was deemed a fair tradeoff.
That said, I am open to accepting a PR that adds a strategy for correct blue noise—if you would like to include it—so I will mark this as an enhancement. Thanks!
The algorithm described in Extreme Learning is incorrect: it does not generate blue noise.
Blue Noise is uniform in high frequency with no low frequency component. While the blog post claims that we need not sample the annulus uniformly, the post provides no justification for this claim. By sampling non-uniformly, the algorithm preferentially generates samples that are (r + epsilon) units apart at the expense of the interval (r + epsilon,2r). This is trading correctness for speed.
The text was updated successfully, but these errors were encountered: