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Compressing Images with Neural Networks

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Re: Compressing Images with Neural Networks

#11
post #7
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.

But that's like saying it's difficult to drive your Formula 1 car to work every day. It's not meant for that, so it's not the car's fault. It's a niche thing built to satisfy the requirements of a niche need. I would suggest this is "you're holding it wrong" type of situations that isn't laughable.

There was absolutely an initiative to make J2K a widespread standard

Re: Compressing Images with Neural Networks

#15
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.

Huh, one more point for considering J2K for film scan archiving.

Re: Compressing Images with Neural Networks

#16
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.

Huh, one more point for considering J2K for film scan archiving.

it's well past the considering stage. J2K is used more than people think even if we're not using to spread cat memes across the interwebs. J2K is used in DCPs sent to movie theaters for digital projections. J2K is used as lossless masters for films. the Library of Congress uses it as well. this isn't even attempting to make an exhaustive list of use, but it's not something being looked into. it's being used every day

Re: Compressing Images with Neural Networks

#18

Earlier quoted context omitted.

Huh, one more point for considering J2K for film scan archiving.

it's well past the considering stage. J2K is used more than people think even if we're not using to spread cat memes across the interwebs. J2K is used in DCPs sent to movie theaters for digital projections. J2K is used as lossless masters for films. the Library of Congress uses it as well. this isn't even attempting to make an exhaustive list of use, but it's not something being looked into. it's being used every day

Well, I meant for me personally. Currently using TIFF. :-)

Re: Compressing Images with Neural Networks

#19
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.

You’re looking for what’s called upscaling, like with Stable Diffusion: https://huggingface.co/stabilityai/stable-diffusion-x4-upsca...

Re: Compressing Images with Neural Networks

#20
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.

There are a bunch of great upscaler models although they tend to hallucinate a bit, I personally use magic-image-refiner:

https://replicate.com/collections/super-resolution

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