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Generative Adversarial Networks for Extreme Learned Image Compression

data.vision.ee.ethz.ch

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Re: Generative Adversarial Networks for Extreme Learned Image Compression

#21

Post by mcbits »

skybrian wrote: post #18
yorwba wrote: post #17

Earlier quoted context omitted.

All lossy compression algorithms hallucinate. That's the whole point: reducing image size by dropping some information and then hallucinating a plausible replacement to decompress. The only difference is that this compression is better at hallucinating, so you don't get ringing artifacts or blocks, but some internally consistent alternate reality. If you don't want to lose data you should not use lossy compression at…

Okay, but still, some kinds of changes are better than others. They should probably start testing for this in the visual perception tests: is lost information greyed out in a visible way? Are words and digits always fuzzed, or replaced? Because it turns out that fuzziness and compression artifacts have a higher-level meaning: when you see them, you know something has been lost. That's an important (if inadvertent) si…

It could be a problem if it hallucinates the wrong license plate number at a crime scene. If all you want is a gigantic 8K resolution stock photo of a woman holding her baby in front of a laptop without devouring 10 MB of the user's data cap, it may be fine if the woman has a slightly different (but still highly detailed) hair style.

Re: Generative Adversarial Networks for Extreme Learned Image Compression

#22

Post by oofabz »

skybrian wrote: post #18
yorwba wrote: post #17

Earlier quoted context omitted.

All lossy compression algorithms hallucinate. That's the whole point: reducing image size by dropping some information and then hallucinating a plausible replacement to decompress. The only difference is that this compression is better at hallucinating, so you don't get ringing artifacts or blocks, but some internally consistent alternate reality. If you don't want to lose data you should not use lossy compression at…

Okay, but still, some kinds of changes are better than others. They should probably start testing for this in the visual perception tests: is lost information greyed out in a visible way? Are words and digits always fuzzed, or replaced? Because it turns out that fuzziness and compression artifacts have a higher-level meaning: when you see them, you know something has been lost. That's an important (if inadvertent) si…

If fuzziness and compression artifacts are a signal, it makes sense that eliminating them would reduce filesize.

Re: Generative Adversarial Networks for Extreme Learned Image Compression

#23

Post by tischler »

skybrian wrote: post #18
yorwba wrote: post #17

Earlier quoted context omitted.

All lossy compression algorithms hallucinate. That's the whole point: reducing image size by dropping some information and then hallucinating a plausible replacement to decompress. The only difference is that this compression is better at hallucinating, so you don't get ringing artifacts or blocks, but some internally consistent alternate reality. If you don't want to lose data you should not use lossy compression at…

Okay, but still, some kinds of changes are better than others. They should probably start testing for this in the visual perception tests: is lost information greyed out in a visible way? Are words and digits always fuzzed, or replaced? Because it turns out that fuzziness and compression artifacts have a higher-level meaning: when you see them, you know something has been lost. That's an important (if inadvertent) si…

I can kind of see regulations coming that make neurally compressed image or video data are require to have a little Ⓝ on-screen graphic in one of the corners, in addition to (not necessarily perceivable) watermarks that can make even small crops of the image identifiable as neurally generated. And that is probably the best case.

In the worst case (and more likely?), we are going to ban computational substrates large enough to perfectly forge important data altogether because it will be too easy to misuse. We‘d essentially go back to ~1960s electronics to have at least halfway functioning mechanisms of creating social trust, namely high-bandwidth personal interactions where every thought and every action has a high chance of leaving a trace in the real world and thus contributing to someone’s reputation. No blockchain and no other technology can create nearly as much trust as that without being highly prone to misuse.

Re: Generative Adversarial Networks for Extreme Learned Image Compression

#24

Post by koliber »

rasz wrote: post #15

Picture is not compressed, its hallucinated from vague memory of the real thing, a mere dream. Cars vanish, building change wall structure, even the license plate receives fake text absent from source materia. Its a giant guesswork of what was there originally. Reminds me of Xerox scanners lying about scanned in numbers http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...

Sounds more or less like our human memory and image recall. Sometimes it's more accurate, but sometimes we make up details that were not there originally.

Re: Generative Adversarial Networks for Extreme Learned Image Compression

#25

Post by dschn_dstryr »

mmastrac wrote: post #2

Just waiting for this to show up in a video compression standard. With the right network it could be just as fast to decompress, though probably insanely slow to compress.

No, the tradeoff is actually the other way around. Encoding with a neural network can potentially be faster than the exhaustive tree searches that are done in current compression methods. On other hand, current decoders are fairly dumb and extremely optimized for speed. Neural networks will probably have trouble competing. In the design of video codecsan increase in decoding time is considered at least 10x more costly than the same increase in encoding time.

source: I worked with both HEVC and neural network based compression.

Re: Generative Adversarial Networks for Extreme Learned Image Compression

#29

Post by stochastic_monk »

I think that the title of the paper should state that it is for lossy image compression, which clearly states how it works and what task it performs.

I would be surprised if there wasn't a way to provide a learned, lossless method of compression, but that would be a very different paper and result.

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