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I can imagine that feeling of "being a cog in a beautiful machine" gets a lot less people these days because so many cogs are automated and so many machines are no longer beautiful.

Bravo! One of my canary projects for LLMs has been a spot color / dithering tool; very much an amateur screen printer, and it seemed a way to spark my own interest to do some multicolor prints. Suffice to say the models couldn’t handle it, but I haven’t checked in a generation or two, so this is great motivation to try again!

> It's fine to dither images but please please admit that you're doing it for the aesthetic

They did in the article.

> My goal was to immitate a printed image.

> Yeah, if you're using lossless compression like PNG or GIF, adding a lossy step to reduce the number of colors in the palette can help.

Depending on the image context it can also backfire, though not so much for photos, but rather screenshots. E.g. sometimes a 2 color dithered image results in a much bigger PNG than, say 16 colors without dithering, because bigger areas of uniform color compress much more efficiently. So I usually just try "everything", to find some sweet spot between file size and quality.

Is there a tool that allows you to select which area of the image to use what dithering parameters and color count for, with a live preview? I know I'll vibecode one myself just to see what it looks like, but maybe this already exists in a "real" application? Then you could e.g. use dithering on the faces, but for the background just a few colors without dithering.

.. What do you mean by "beats" here? Is the article trying to hide the fact that they're doing dithering for, among other things, artistic reasons?

A dithered image almost certainly beats a non-dithered JPEG compressed image in terms of file size, because you can JPEG compress the dithered image. Thus, if you like the look of a dithered image, you get file size reduction as a bonus.

I swear, some people on HN intentionally misinterpret and misrepresent the text of an article just to argue against it in bad faith with these smarmy "um actually" type arguments. For some reason I do not understand, Hacker News tends to surface these comments near the top of its comment sections.

That’s a hn filter to avoid clickbait titles I think

Yes, which of course is still cool. Probably the main point.

Yeah this is important when you print images, since printers can reveal banding artifacts you can’t see on a monitor. I learned this the expensive way printing a batch of large format images that had gradients in them, without dithering. This is also how I learned one way that Photoshop was for professionals and GIMP & ImageMagick weren’t; At the time (and this was years ago, to be fair), Photoshop dithered by default when going from 16 to 8 bits, while those popular open source tools did not and also did not even have good ways to do it manually by adding the right amount of noise before changing the bit depth. I don’t know what the open source tools do today, maybe they’ve fixed it, I haven’t checked.

The counterpoint is that you should just keep your images at 10 bit minimum or 12+ bit until the OS/monitor can dither for you. Lossy compression will do much better with a 10/12 bit image than a dithered 8 bit one.

I'm only vaguely familiar with dithering/half-toning. What does "component" refer to here?

I’d say halftoning is a specific type of dithering. Wikipedia mentions that for print, some people consider the terms synonymous.

https://en.wikipedia.org/wiki/Dither#Applications

See also,

Surface-stable fractal dithering:

https://www.youtube.com/watch?v=EzjWBmhO_1E

And the technical deep dive:

https://www.youtube.com/watch?v=HPqGaIMVuLs

>Is this even dithering?

>But if you care about the method this is not dithering.

> I looked it up, the translation in this case is, in fact, simply "half tones" :^)

It's a somewhat active inverse problems research subfield https://github.com/MenghanXia/ReversibleHalftoning

Dither loses information, so perfectly reversible might be an unreasonable goal. But I bet you could train or fine-tune a denoiser to do a really good job. This might make a cool paper, if there’s anything at all non-standard or surprising you discover along the way.

No, although AI gets really close. Dithering represents adding noise to an image and also represents doing a lossy type of compression.

Do you want to undither, or un-half-tone, like the images in TFA?

Unhalftoning is called descreening.

I use this:

https://descreen.net/

It's an aesthetic choice. What counters taste?

That only says to not expect compression from dither, it’s not a counterpoint to using dither for any other reason.

Perhaps because inference on dithered images is pretty rare, not a common or popular thing to do? Otherwise, people would train on dithered images. NNs are trained on noisy images. Training with dither would not learn any other invariants besides how to see through dither, it doesn’t automatically generalize to anything else.

When someone says AI can't do something, I like to try it:

> This image features a close-up abstract scene styled with a classic halftone (Ben-Day dot) pop-art filter ...

https://share.gemini.google/O5eqZOKxwzw3

> This image is a halftone/thermal print styled photograph. The photo features a group of four people posing closely together indoors or in front of a windowed backdrop:

https://share.gemini.google/KJOxBDXYsFT4

I’m sorry, but I can’t understand what you’re saying at all. Explain?