Comment by vanderZwan

Thanks for answering! I agree that in the one example image in the readme the output looks quite natural. In other implementations I've seen I could always notice "ghosts" of the Hilbert curve in the resulting image, as a subtle edge artifact. So that turned me off of the algorithm a bit, even though I find it a very elegant approach to dithering. Usually the other images were 1 bit though, that might have been a factor.

On the note of matrix based error diffusion and exploring other methods: maybe you'd enjoy Victor Ostromoukhov's variable coefficient dithering paper[0]. Instead of one diffusion matrix, it has different diffusion matrix depending on the value of the input pixel, and the result is a much more blue noise-like dithering.

Given that his paper is almost a quarter century old I've been wondering if we could find better matrices using modern solver algorithms on today's hardware. I've never used a solver myself so wouldn't know how to set this up though.

Also, there's Zhou-Fang dithering, which takes Ostromoukhov's algorithm and introduces a little bit of randomness to remove artifacts[1]. I have JavaScript implementations for both algorithms in an Observable notebook if you want to try them out[2]. It's limited to 1-bit output though.

[0] https://perso.liris.cnrs.fr/victor.ostromoukhov/publications...

[1] https://dl.acm.org/doi/abs/10.1145/1201775.882289

[2] https://observablehq.com/@jobleonard/variable-coefficient-di...

big-nacho 5 days ago

Two things: 1. I like the natural feel it gives to images. 2. Easy to control. As much as it's super easy to cook up a matrix based error diffusion dither (like Floyd), there are a lot of things to take care of to reduce artifacts and bad side effects.

I also generally want to take a bit more time with the dithering topic and explore other methods too, which hopefully I'll add in the future.

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vanderZwan 4 days ago

Thanks for answering! I agree that in the one example image in the readme the output looks quite natural. In other implementations I've seen I could always notice "ghosts" of the Hilbert curve in the resulting image, as a subtle edge artifact. So that turned me off of the algorithm a bit, even though I find it a very elegant approach to dithering. Usually the other images were 1 bit though, that might have been a factor.
On the note of matrix based error diffusion and exploring other methods: maybe you'd enjoy Victor Ostromoukhov's variable coefficient dithering paper[0]. Instead of one diffusion matrix, it has different diffusion matrix depending on the value of the input pixel, and the result is a much more blue noise-like dithering.
Given that his paper is almost a quarter century old I've been wondering if we could find better matrices using modern solver algorithms on today's hardware. I've never used a solver myself so wouldn't know how to set this up though.
Also, there's Zhou-Fang dithering, which takes Ostromoukhov's algorithm and introduces a little bit of randomness to remove artifacts[1]. I have JavaScript implementations for both algorithms in an Observable notebook if you want to try them out[2]. It's limited to 1-bit output though.
[0] https://perso.liris.cnrs.fr/victor.ostromoukhov/publications...
[1] https://dl.acm.org/doi/abs/10.1145/1201775.882289
[2] https://observablehq.com/@jobleonard/variable-coefficient-di...

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