That scene gets replayed in my mind three or four times a month.

Han shot first.

In the context of video encoding, any manufactured/hallucinated detail would count as "loss". Loss is anything that's not in the original source. The loss you see in e.g. MPEG4 video usually looks like squiggly lines, blocky noise, or smearing. But if an AI encoder inserts a bear on a tricycle in the background, that would also be a lossy compression artifact in context.

As for frame interpolation, it could definitely be better, because the current algorithms out there are not good. It will not likely be more popular, since this is generally viewed as an artistic matter rather than a technical matter. For example, a lot of people hated the high frame rate in the Hobbit films despite the fact that it was a naturally high frame rate, filmed with high-frame-rate cameras. It was not the product of a kind-of-shitty algorithm applied after the fact.

It's an interesting thought experiment, but we don't actually see what really exists, our brains essentially are AI vision, filling in things we don't actually perceive. Examples are movement while we're blinking, objects and colors in our peripheral vision, the state of objects when our eyes dart around, etc.

The difference is we can't go back frame by frame and analyze these "hallucinations" since they're not recorded. I think AI enhanced video will actually bring us closer to what humans see even if some of the data doesn't "exist", but the article is correct that it should never be used as evidence.

There are plenty of lossless codecs already

It remains to be seen, of course, but I expect to be able to get lossless (or nearly-lossless) video at a much lower bitrate, at the expense of a much larger and more compute/memory-intensive codec.

The way I see it working is that the codec would include a general-purpose model, and video files would be encoded for that model + a file-level plugin model (like a LoRA) that's fitted for that specific video.

I think there's a possibility for long format video of stable scenes to use ML for higher compression ratios by deriving a video specific model of the objects in the frame and then describing their movements (essentially reducing the actual frames to wire frame models instead of image frames, then painting them in from the model).

But that's a very specific thing that probably only work well for certain types of video content (think animated stuff)

Nvidia's rtx video upscaling is trying to be just that: DLSS but you run it on a video stream instead of a game running on your own hardware. They've posited the idea of game streaming becoming lower bit rate just so you can upscale it locally, which to me sounds like complete garbage

No, I think you misunderstood what I'm trying to say. We already have tech that uses machine learning to upscale stuff in real-time, but I'm not that it's accurate on things like court videos. I don't think we'll ever get to a point where it can be accurate as evidence because by the very nature of the tech it's making up detail, not enhancing it. You can't enhance what isn't there. It's not turning nothing into accurate data, it's guessing based on input and what it's been trained on.

Prime example right here, this is the objectively best version of Alice in Wonderland, produced by BBC in 1999, and released on VHS. As far as I can tell there was never a high quality version available. Someone used machine learning to upscale it, and overall it looks great, but there are scenes (such as the one that's linked) where you can clearly see the flaws. Tina Majorino has no face, because in the original data, there wasn't enough detail to discern a face.

Now we could obviously train a model to recognise "criminal activity", like stabbing, shooting, what have you. Then, however, you end up with models that mistake one thing for another, like scratching your temple turning into driving while on the phone, now if instead of detecting something, the model's job is to fill in missing data we have a recipe for disaster.

Any evidence that has had machine learning involved should be treated with at least as much scrutiny as a forensic sketch, while while they can be useful in investigations, generally don't carry much weight as evidence. That said, a forensic sketch is created through collaboration with an artist and a witness, so there is intent behind those. Machine generated artwork lacks intent, you can tweak the parameters until it generates roughly what you want, but it's honestly better to just hire an artist and get exactly what you want.

Security (or other) cameras don’t have access to this extra data

Samsung's AI on their latest phones and tablets does EXACTLY what @MrPoopbutt@lemmy.world is describing. It will literally create data including parts of scenes and even full frames, in order to make video look better.

So while a true security camera may not be able to do it there's now widely available consumer products that WILL. You're also forgetting that even Security Camera footage can be processed through software so footage from those isn't immune to AI fiddling either.