We used to create things that were trying to simulate (reproduce) reality, but now we are using those "simulations" we'd created as if they were the real thing. With time we will be getting farther away from the "truth" (as you put it), and yes - I share your worry about that.
https://en.wikipedia.org/wiki/Simulacrum
EDIT: A good example I heard that explains what a simulacrum is was this: Ask a random person to draw a photo of a princes and see how many will draw a disney princess (which already was based on real princesses) vs how many will draw one looking like Catherine of Aragon or another real princess.
Invisibly changing the content rather than the image quality seems like a really concerning failure mode for image compression!
I wonder if it'd be possible to use SD as part of a lossless system - use SD as something that tells us the liklihood of various pixel values given the rest of the image and combine that liklihood with a huffman encoding. Either way, fantastic hack, but we really should avoid using anything lossy built on AI for image compression.
But it'd definitely be cool to have some latent representation of a video that then gets rendered on tv - you could apply latent style sheets to the content, like what actors you want to play the roles, or turn everything into a steam-punk anime on the fly. The more abstract the representation, the more interesting alterations you could apply
And… Some manufacturer apparently already did it on their ultra zoom phones when taking photos of the moon.
2/3 of the image is just dreamed up by the ISP (image signal processor) when it debayers the raw image.
I'm not aware of any consumer hardware that has open source ISP firmware or claims to optimize for accuracy over beauty.
“When used in lossy mode, JBIG2 compression can potentially alter text in a way that's not discernible as corruption. This is in contrast to some other algorithms, which simply degrade into a blur, making the compression artifacts obvious.[14] Since JBIG2 tries to match up similar-looking symbols, the numbers "6" and "8" may get replaced, for example.
In 2013, various substitutions (including replacing "6" with "8") were reported to happen on many Xerox Workcentre photocopier and printer machines, where numbers printed on scanned (but not OCR-ed) documents could have potentially been altered. This has been demonstrated on construction blueprints and some tables of numbers; the potential impact of such substitution errors in documents such as medical prescriptions was briefly mentioned.”
During my PhD this issue came up amongst those in the group looking into compressed sensing in MRI. Many reconstruction methods (AI being a modern variant) work well because a best guess is visually plausible. These kinds of methods fall apart when visually plausible and "true" are different in a meaningful way. The simplest examples here being the numbers in scanned documents, or in the MRI case, areas of the brain where "normal brain tissue" was on average more plausible than "tumor".
[1]: http://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres_...
Compression that gives you a blurred image is a trade-off.
But what does it mean to “be aware of” compression that may give you a crisp image of some made up document?
This isn't an obvious statement to me. If you've had the misfortune of scanning documents to PDF and getting the 100MB per page files automatically emailed to you then you might see the benefit in all that white space being compressed somehow.
> But what does it mean to “be aware of” compression that may give you a crisp image of some made up document?
This isn't something I said. A good compression system for documents will not change characters in any circumstances.
idk if I had to second guess every single result coming out of a machine it would be a showstopper for me. This isn't pokemon go, tumor detection is serious matter
But say you're doing noisy measurements, and you are under-measuring like you say, and you have to fabricate non-random non-homogenous reconstruction noise. In that case it would be a very good idea to produce, as they do for lossy compression, both the standard overall bit rate vs. PSNR characterization against alternate direct (non-sparse) measurement ground truths (that have to exist, or else the reconstruction method should be called into question), and the bit rate for each particular sparse measurement. So this way people can see how reliable the reconstruction is. Ideally the image should be labeled at the pixel level with reconstruction probabilities, or presented in other ways to demonstrate the ratio of measured vs. fabricated information, like 95% confidence-interval extremal reconstructions or something.
It's not clear that community is doing this level of due diligence, so then the voices here are right: it's not a good idea to use.
It's perfectly possible to build neural network based compression systems that do not output false information.
IE, the algorithm ignores and loses the 'irrelevant' information, but holds the important stuff?