https://en.m.wikipedia.org/wiki/Hutter_Prize
A lossless compression contest to encourage research in AI. It's lossless, I think just to standardize scoring, but I always thought a lossy version would be better for AI -- our memories are definitely lossy!
Almost. Compression and AI both revolve around information processing, but their core objectives diverge. Compression is focused on efficient representation, while AI is built for flexibility and the ability to navigate the unpredictable aspects of real-world data.
Compression learns a representation from the same data it encodes, like "testing on the training set". AI models have different training and test data. There are no surprises in compression.
I like your sentiment, it is technically inspiring.
“On the other hand, I can generate endless amounts of Harlan Coben miniseries… :-P”
[1] https://en.m.wikipedia.org/wiki/Sloot_Digital_Coding_System
Guy named Borges already patented that, I'm afraid.
From the description, it looks like it's only being tested with 128x128 frames, which implies that the speed is very low.
It can? Maybe I'm misunderstanding the graphs but it doesn't look like it to me?
Many older/commercial video codecs optimized for PSNR, which results in the output being blurry and textureless because that's the best way to minimize rate for the same PSNR.
Even with that, showing H.265 having lower PSNR than H.264 is odd --- it's the former which has often looked blurrier to me.
I guess where this sort of generative video "compression" is headed is that the video would be the prompt, and you'd need a 100GB decoder (model) to render it.
No doubt one could fit a prompt to generate a movie similar to something specific in a floppy size ("dude gets stuck on mars, grows potatoes in his own shit"). However, 1MB is only enough to hold the words of a book, and one could imagine 100's of movie adaptations (i.e. visualizing the "prompt") of any given book that would all be radically different, so it seems a prompt of this size would only be enough to generate one of these "prompt movie adaptations".
As a casual non-scholar, non-AI person trying to parse this though, it's infuriatingly convoluted. I was expecting a table of "given source file X, we got file size Y with quality loss Z", but while quality (SSIM/LPIPS) is compared to standard codecs like H.264, for the life of me I can't find any measure of how efficient the compression is here.
Applying AI to image compression has been tried before though, with distinctly mediocre results: some may recall the Xerox debacle about 10 years, when it turned out copiers were helpfully "optimizing" images by replacing digits with others in invoices, architectural drawings, etc.
https://www.theverge.com/2013/8/6/4594482/xerox-copiers-rand...
This is not even AI. JBIG2 allows a reuse of once-decoded image patches because it's quite reasonable for bi-level images like fax documents. It is true that similar glyphs may be incorrectly groupped into the same patch, but such error is not specific to patch-based compression methods (quantization can often lead to the same result). The actual culprit was Xerox's bad implementation of JBIG2 that incorrectly merged too many glyphs into the same patch.
Furthermore, it's pretty common in compression research to focus on the size/quality trade-off, and leave optimization of compute for real-world implementations.
Which have examples in it.
Unfortunately it only contains still images with teeny thumbnails: https://arxiv.org/html/2402.08934v1/x2.png
Is this more extreme than youtube ?
