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Comment by dusted | Hacker News Reader

That's a lot worse than the original.

I agree. The upscaled version is both more smoothed and more mottled. This matches the "look" of a lot of low bitrate HD video, but I would argue that information has actually been lost in the upscaling process, not gained. It would be much easier to tell with before / after screenshots, I think, but at any rate I don't see a lot of reason to hope neural net upscaling will be useful for many "real world" purposes.

The HFR was pretty convincing to me, however.

You can see comparison at the end of the video

You're right. Unfortuate that the comparison clip is so short, and there's really no replacement for being able to flip between individual upscaled frames. From that very limited comparison, I think I still prefer the original.

I agree with your points, but I'd also argue that Nvidias DLSS implementation is a real world purpose, no?

That's a great point. Using approaches like this as a less computationally complex method of resampling for the purpose of anti-aliasing (and not upscaling) seems worthwhile, definitely.

I was thinking more of attempts to create neural nets that map from e.g. the set of 480p images to the set of 1080p images. The "best case" results seem to be trained on low bitrate HD video, which gives results that "look good" to many people (especially those who grew up watching Youtube) but in terms of real detail are worse than a simple upscale (with e.g. Lanczos). I haven't yet seen results where content aware upscaling provides a real improvement over "dumb" algorithms for this purpose.

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