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Compressing Images with Neural Networks

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Re: Compressing Images with Neural Networks

#31

How badly will its lossy-ness change critical things? In 2013, there were Xerox copiers with aggressive compression that changed numbers, https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...

If I zoom all the way with my iPhone, the camera-assisting intelligence will mess up numbers too

Re: Compressing Images with Neural Networks

#33
post #26
post #6

Earlier quoted context omitted.

Yes, I do mean broad- rather than niche adoption. I myself used J2K to archive film scans. One problem is that without broad adoption, support even in niche cases is precarious; the ecosystem is smaller. That makes the codec not safe for archiving, only for distribution. The strongest use case I see for this is streaming video, where the demand for compression is highest.

For archiving, I'd recommend having a wasm decompressor along with some reference output. Could also ship an image viewer as an html file with all the code embedded.

Why the need for all things to be browser based? Why introduce the performance hit for something that brings no compelling justification? What problem is this solution solving? Why can't things just be native workflows and not be shoveled into a browser?

Re: Compressing Images with Neural Networks

#35

How badly will its lossy-ness change critical things? In 2013, there were Xerox copiers with aggressive compression that changed numbers, https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...

If I zoom all the way with my iPhone, the camera-assisting intelligence will mess up numbers too

The mentioned Xerox copier incident was not an OCR failure, but the copier actively changed the numbers in the original image due to its image compression algorithm.

Re: Compressing Images with Neural Networks

#36
post #10

Anyone know of open models useful (and good quality) for going the other way? I.e., Input is a 800x600 jpg and output is 4k version.

Magnific.ai ( https://magnific.ai ) is a paid tool that works well, but it is expensive. However, this weekend someone released an open-source version which has a similar output. ( https://replicate.com/philipp1337x/clarity-upscaler ) I'd recommend trying it. It takes a few tries to get the correct input parameters, and I've noticed anything approaching 4× scale tends to add unwanted hallucinations. For example, I ha…

Here's free and open source alternative that works pretty well

https://www.upscayl.org/

Re: Compressing Images with Neural Networks

#37
post #6
post #5

Earlier quoted context omitted.

Did JPEG2000 really flounder? If your concept of it being a consumer facing product as a direct replacement for JPEG, then I could see being unsuccessful in that respect. However, JPEG2000 has found its place in the professional side of things.

Yes, I do mean broad- rather than niche adoption. I myself used J2K to archive film scans. One problem is that without broad adoption, support even in niche cases is precarious; the ecosystem is smaller. That makes the codec not safe for archiving, only for distribution. The strongest use case I see for this is streaming video, where the demand for compression is highest.

That makes the codec not safe for archiving, only for distribution.

Could you explain what you mean by "not safe for archiving"? The standard is published and there are multiple implementations, some of which are open-source. There is no danger of it being a proprietary format with no publicly available specification.

Re: Compressing Images with Neural Networks

#38
post #6

Earlier quoted context omitted.

Yes, I do mean broad- rather than niche adoption. I myself used J2K to archive film scans. One problem is that without broad adoption, support even in niche cases is precarious; the ecosystem is smaller. That makes the codec not safe for archiving, only for distribution. The strongest use case I see for this is streaming video, where the demand for compression is highest.

That makes the codec not safe for archiving, only for distribution. Could you explain what you mean by "not safe for archiving"? The standard is published and there are multiple implementations, some of which are open-source. There is no danger of it being a proprietary format with no publicly available specification.

Not the GP, but for archiving, you want to know that you'll be able to decode the files well into the future. If you adopt a format that's not well accepted and the code base gets dropped and not maintained so that in the future it is no longer able to be run on modern gear, your archive is worthless.

As a counter, J2K has been well established by the professional market even if your mom doesn't know anything about what it is. It has been standardized by the ISO, so it's not something that will be forgotten about. It's a good tool for the right job. It's also true that not all jobs will be the right ones for that tool

Re: Compressing Images with Neural Networks

#39
post #35

Earlier quoted context omitted.

If I zoom all the way with my iPhone, the camera-assisting intelligence will mess up numbers too

The mentioned Xerox copier incident was not an OCR failure, but the copier actively changed the numbers in the original image due to its image compression algorithm.

Here's some of the context: www.dkriesel.com/blog/2013/0810_xerox_investigating_latest_mangling_test_findings

Learn More: https://www.dkriesel.com/start?do=search&id=en%3Aperson&q=Xe...

Brief: Xerox machines used template matching to recycle the scanned images of individual digits that recur in the document. In 2013, Kriesel discovered this procedure was faulty.

Rationale: This method can create smaller PDFs, advantageous for customers that scan and archive numerical documents.

Prior art: https://link.springer.com/chapter/10.1007/3-540-19036-8_22

Tech Problem: Xerox's template matching procedure was not reliable, sometimes "papering over" a digit with the wrong digit!

PR Problem: Xerox press releases initially claimed this issue did not happen in the factory default mode. Kriesel demonstrated this was not true, by replicating the issue in all of the factory default compression modes including the "normal" mode. He gave a 2015 FrOSCon talk, "Lies, damned lies and scans".

Interesting work!

Re: Compressing Images with Neural Networks

#40
post #26

Earlier quoted context omitted.

For archiving, I'd recommend having a wasm decompressor along with some reference output. Could also ship an image viewer as an html file with all the code embedded.

Why the need for all things to be browser based? Why introduce the performance hit for something that brings no compelling justification? What problem is this solution solving? Why can't things just be native workflows and not be shoveled into a browser?

Not the parent but one imagines that WASM could be a good target for decompressing or otherwise decoding less-adopted formats/protocols because WASM is fairly broadly-adopted and seems to be at least holding steady if not growing as an executable format: it seems unlikely that WASM disappears in the foreseeable future.

Truly standard ANSI C along with a number of other implementation strategies (LLVM IR seems unlikely to be going anywhere) seem just as durable as WASM if not more, but there are applications where you might not want to need a C toolchain and WASM can be a fit there.

One example is IIUC some of the blockchain folks use WASM to do simultaneous rollout of iterations to consensus logic in distributed systems: everyone has to upgrade at the same time to stay part of the network.

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