Charlie Loyd @vruba@everything.happens.horse

It’s pretty old-fashioned now. The vanilla DWT, the Tudor-era entropy coder – with only admiration for what it was in 2003, no one would propose a new format like ICER today. But it’s interesting in itself and as a what-if. (What if JPEG 2000 were designed for normal people, basically.)

I was just saying to a friend that JPEG 2000 never succeeded in supplanting JPEG outside a few high-end niches (mapping, archives, medical imaging) due to a scope-crept, complex, IP-unclear, and generally unappealing-to-nonspecialist-developers design, but I think there’s an alternate history not too far away in the multiverse where someone adapted ICER into an OS and browser-supported JPEG replacement in, like, 2005.

https://github.com/TheRealOrange/icer_compression is pretty neat – a mostly complete implementation of ICER, a wavelet-based, error-resilient image compression format designed by NASA 20+ years ago for Spirit and Opportunity.

Based on informal tinkering and reading. Happy to discuss, within reason.

1. QKV attention transformers work but are gross – O(n²) and equivariance problems – and will not last.

2. Most – not all! – learned convolutions are wasted and should be replaced by fixed bases or frames, or at least grouped convolutions.

3. Diffusion and flow training approaches are immature but way more elegant than x→y training.

5. MMA regularization is good.

One of my favorite things on the Internet is when you watch a “Columbo” clip and there’s a comment where someone describes the concept of Columbo at a second-grade level and is massively upvoted for it. This sounds wry and ironical but I do honestly enjoy it.

I’m tinkering with something where shearlets are very clearly The Right Tool For The Job. There is one pytorch library implementing them in what looks like a good way. Its last commit was 3 years ago and it’s not on PyPI; turns out you have to compile it locally against the CUDA SDK. Using it would instantly make my project unportable. I’m not complaining about the author of the library – if anything, I’m grateful to them. But I’m certainly complaining about the situation.

Waking up early, going on a walk, making breakfast, and then spending two hours meditating on how much I wish someone would write some good, differentiable, richly featured wavelet libraries in python.

Importing libraries: 0.1 seconds
Data loading/decoding: 0.25 seconds
Array manipulation: 0.03 seconds
Optical flow algorithm that does not work: 475 seconds
Writing data: 0.35 seconds

someone who is good at optimization please help me

Like if you see something real and find yourself wanting to think or say “Haha, just like in the movies/books/longform fiction podcasts”, interrogate that impulse at least medium-hard.

A good behavioral rule of thumb, in my experience, is to avoid reflexively comparing real life to entertainment.

This is what I’m running:

yt-dlp --user-agent "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:124.0) Gecko/20100101 Firefox/124.0" --cookies-from-browser firefox -f 311 "https://www.youtube.com/watch?v=P9C25Un7xaM"

I assume the problem here is that YT runs stuff browserside to negotiate up to the good stream, but I don’t know.

Any tips on saving a YouTube stream (specifically, the ISS camera feed)? What yt-dlp saves is much lower bitrate than what I request it to fetch, and this seems to be a known bug of sorts.