zlacker

What type of compression would change the relative scale of elements within an image? None that I'm aware of, and these platforms can't really make up new video codecs on the spot since hardware accelerated decoding is so essential for performance.

Excessive smoothing can be explained by compression, sure, but that's not the issue being raised there.

>>jshear+(OP)
AI models are a form of compression.

Neural compression wouldn't be like HVEC, operating on frames and pixels. Rather, these techniques can encode entire features and optical flow, which can explain the larger discrepancies. Larger fingers, slightly misplaced items, etc.

Neural compression techniques reshape the image itself.

If you've ever input an image into `gpt-image-1` and asked it to output it again, you'll notice that it's 95% similar, but entire features might move around or average out with the concept of what those items are.

>>echelo+51
Maybe such a thing could exist in the future, but I don't think the idea that YouTube is already serving a secret neural video codec to clients is very plausible. There would be much clearer signs - dramatically higher CPU usage, and tools like yt-dlp running into bizarre undocumented streams that nothing is able to play.

>>jshear+(OP)
> What type of compression would change the relative scale of elements within an image?

Video compression operates on macroblocks and calculates motion vectors of those macroblocks between frames.

When you push it to the limit, the macroblocks can appear like they're swimming around on screen.

Some decoders attempt to smooth out the boundaries between macroblocks and restore sharpness.

The giveaway is that the entire video is extremely low quality. The compression ratio is extreme.

>>jshear+x1
A new client-facing encoding scheme would break utilization of hardware encoders, which in turn slows down everyone's experience, chews through battery life, etc. They won't serve it that way - there's no support in the field for it.

It looks like they're compressing the data before it gets further processed with the traditional suite of video codecs. They're relying on the traditional codecs to serve, but running some internal first pass to further compress the data they have to store.

>>jshear+x1
If they were using this compression for storage on the cache layer, it could allow more videos closer to where they serve them, but they decide the. Back to webm or whatever before sending them to the client.

I don't think that's actually what's up, but I don't think it's completely ruled out either.

>>planck+i4
That doesn't sound worth it, storage is cheap, encoding videos is expensive, caching videos in a more compact form but having to rapidly re-encode them into a different codec every single time they're requested would be ungodly expensive.

>>jshear+H4
Storage gets less cheap for short-form tiktoks where the average rate of consumption is extremely high and the number of niches is extremely large.

>>echelo+51
The resources required for putting AI <something> inline in the input (upload) or output (download) chain would likely dwarf the resources needed for the non-AI approaches.

>>jshear+(OP)
One that represented compressed videos as an embedding that gets reinflated by having gen AI interpret it back into image frames.

>>jshear+H4
The law of entropy appears true of TikToks and Shorts. It would make sense to take advantage of this. That is to say, the content becomes so generic that it merges into one.

>>Aurorn+G2
They're doing something with neural compression, not classical techniques.

https://blog.metaphysic.ai/what-is-neural-compression/

See this paper:

https://arxiv.org/abs/2412.11379

Look at figure 5 and beyond.

Here's one such Google paper:

https://c3-neural-compression.github.io/