Wikipedia calls blue noise dithering a form of ordered dithering. Error diffusion indeed looks similar to blue noise dithering, but the amount of preserved detail of blue noise dithering is more like that of Ordered Bayer, lower than error diffusion. At least in the example images on Wikipedia: https://en.wikipedia.org/wiki/Dither

If I CTRL+F for "blue noise" on the wiki page I can find nothing suggesting that (and I promise I haven't ninja-edited anything), so I think there is a misunderstanding somewhere. Could you please cite the passage that gave you this impression, so we can clear up how we end up drawing different conclusions?

EDIT: Oh, I think I see the source of confusion:

> A matrix tuned for blue noise, such as those generated by the void-and-cluster method, produces a look closer to that of an error diffusion dither method.

If you use a dithering matrix it is by definition ordered, yes. Because it is tiling that matrix in a regular pattern. The matrix has been randomized via a blue-noise method. The test image seems to use a 16 by 16 matrix, which is very small - the seams are clearly recognizable.

However, "blue noise dithering" typically refers to generating a blue noise texture as large as the image to be dithered. This article does so, for example:

https://surma.dev/things/ditherpunk/

But yeah, I can understand the confusion now. The wording of the wiki article could be clearer.

PS: in case your curiosity has been piqued, here are some more articles on blue noise and dithering:

https://blog.demofox.org/2018/01/30/what-the-heck-is-blue-no...

https://blog.demofox.org/2019/06/25/generating-blue-noise-te...

Okay, but in the examples in the first link, standard error diffusion (Floyd-Steinberg and Jarvis-Judice-Ninke) still preserves more fine detail than the blue noise method. They only have the disadvantage of creating some line artifacts for gradients into pure white or pure black.

It would have been interesting if the author did additionally apply a low pass (Gaussian blur) filter to all the result images, not just to one homogeneous one in the beginning.