Every example I thought "yeah, this is cool, but I can see there's space for improvement" — and lo! did the author satisfy my curiosity and improve his technique further.
Bravo, beautiful article! The rest of this blog is at this same level of depth, worth a sub: https://alexharri.com/blog
What a great post. There is an element of ascii rendering in a pet project of mine and I’m definitely going to try and integrate this work. From great constraints comes great creativity.
It's important to note that the approach described focuses on giving fast results, not the best results.
Simply trying every character and considering their entire bitmap, and keeping the character that reduces the distance to the target gives better results, at the cost of more CPU.
This is a well known problem because early computers with monitors used to only be able to display characters.
At some point we were able to define custom character bitmap, but not enough custom characters to cover the entire screen, so the problem became more complex.
Which new character do you create to reproduce an image optimally?
And separately we could choose the foreground/background color of individual characters, which opened up more possibilities.
Thinking more about the "best results". Could this not be done by transforming the ascii glyphs into bitmaps, and then using some kind of matrix multiplication or dot production calculation to calculate the ascii character with the highest similarity to the underlying pixel grid? This would presumably lend itself to SIMD or GPU acceleration. I'm not that familiar with this type of image processing so I'm sure someone with more experience can clarify.
You said “best results”, but I imagine that the theoretical “best” may not necessarily be the most aesthetically pleasing in practice.
For example, limiting output to a small set of characters gives it a more uniform look which may be nicer. Then also there’s the “retro” effect of using certain characters over others.
And a (the?) solution is using an algorithm like k-means clustering to find the tileset of size k that can represent a given image the most faithfully. Of course that’s only for a single frame at a time.
In the appendix, he talks about reducing the lookup space by quantising the sampled points to just 8 possible values. That allowed him to make a look up table about 2MB in size which were apparently incredibly fast.
I've been working on something similar (didn't get to this stage yet) and was planning to do something very similar to the circle-sampling method but the staggering of circles is a really clever idea I had never considered. I was planning on sampling character pixels' alignment along orthogonal and diagonal axes. You could probably combine these approaches. But yeah, such an approach seemed particularly powerful for the reason you could encode it all in a table.
Bravo, beautiful article! The rest of this blog is at this same level of depth, worth a sub: https://alexharri.com/blog
How do you arrive at that? It's presented like it's a natural conclusion, but if I was trying to adjust contrast... I don't see the connection.
https://github.com/cacalabs/libcaca
Simply trying every character and considering their entire bitmap, and keeping the character that reduces the distance to the target gives better results, at the cost of more CPU.
This is a well known problem because early computers with monitors used to only be able to display characters.
At some point we were able to define custom character bitmap, but not enough custom characters to cover the entire screen, so the problem became more complex. Which new character do you create to reproduce an image optimally?
And separately we could choose the foreground/background color of individual characters, which opened up more possibilities.
For example, limiting output to a small set of characters gives it a more uniform look which may be nicer. Then also there’s the “retro” effect of using certain characters over others.