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Comment by smitec | Hacker News Reader

There is a talk about that issue [1].

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_...

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It's worth noting that these problems are things to be aware of, not the complete showstoppers some people seem to think that they are.

I'm having a hard time seeing where the random substitution of all numbers isn't supposed to be a complete showstopper.

Well for example you train the VAE to reduce the compression on characters.

The right amount of compression in a photocopy machine is zero.

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?

If you are making an image of a cityscape to illustrate an article it probably doesn't matter what the city looks like. But if the article is about the architecture of the specific city, it probably does, so you need to 'be aware' that the image you are showing people isn't correct, and reduce the compression.

This subthread was about changing numbers in scanned documents and vanishing tumors in medical images.

> The right amount of compression in a photocopy machine is zero.

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.

> not the complete showstoppers some people seem to think that they are.

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

Why would you want to lossily compress any medical image is beyond me. You get equipment to make precise high-resolution measurements, it goes without saying that you do not want noise added to that.

In medical images, you don't record first and then compress later. Instead, you make sparse measurements and then reconstruct. Why? Because people move, so getting more frames/sec is a thing; you don't want people to stay for too long in the machine; and (ideally) with the same setup, you can focus on a smaller area and get a higher resolution than standard measurements too.

You are talking about compressed sensing which is not lossy compression (compressed sensing can be lossless unless you're dealing with noisy measurements).

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.

An medical sensor filling in "plausible" information is not a show stopper? I hope you are never in control of making decisions like that.

To be aware of when you are building compression systems.

It's perfectly possible to build neural network based compression systems that do not output false information.

If the compression is lossless that's fine. I have not seen an AI system being used in this manner but I don't doubt it's possible. All lossy compression methods output false information, that's the point of lossy compression and why it works so well. Remove details that the compression algorithm deems unimportant.

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