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Comment by daniel_rh | Hacker News Reader

It's very easy to make a JPEG file that will not compress at all. Luckily it might look like snow from a television set rather than a typical image produced by a camera.

We live in a world where it is common for blue sky to occupy a portion of the frame and green grass to occupy another portion of the frame. Since images captured of our world exhibit repetition and patterns, there are opportunities for lossless compression that focuses on serializing deviations from the patterns.

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Impressive work, Daniel! Do I understand correctly that any image prediction for which the deltas are smaller in absolute value than the full JPEG/DCT coefficients would offer continued compression benefits? As in, if you could "name that tune" to predict the rest of the entire image from the first few pixels, the rest of the image would be stored for close to free (and if not, it essentially falls back to regular JPEG encoding).

If that's the case, then not only could we rely on the results of everything we've decompressed so far to use for prediction (which is like one-sided image in-painting), but we also could store a few bits of semantic information (e.g. from an image-net-based CNN, from face detection) about the content of the original image before re-compression, and use that semantic information for prediction as well via some generative model. All of this would obviously be trading computation for storage/bandwidth, but it this seems like an exciting direction to me. Again, nice work.

Hi GrantS: That's pretty close to how it works. We always use the new prediction, even where it's worse than JPEG though (very rarely), to stay consistent.

As for having the mega-model that predicts all images better: well it turns out with the lepton model out you only lose a few tenths of a percent by training the model from scratch on each images individually. We have a test case for training a global model in the archive (it's https://github.com/dropbox/lepton/blob/master/src/lepton/tes... ) That trains the "perfect" lepton model on the current image then uses that same model to compress the image (It's not meant to be a fair test, but it gives us a best-case scenario for potential gains from a model that has been trained from a lot of images) and in this case it doesn't gain much, even in a controlled situation like the test suite.

However the idea you mention here may still be a good idea for a hypothetical model--but we haven't identified that model yet.

I think you are explaining how both lossy and lossless compression work without explaining how this does it differently.

I can't read the article due to technical constraints, but understand that e.g. JPEG has a lossy quantisation pass followed by a lossless encoding/compression pass of the result of the first stage. If they're reproducing bit-identical result to the input JPEG, it must be a (very good) optimisation of the latter stage. [How'd I do?]

JPEG has a very poor lossless encoding stage; this is well known. Several archiving tools (most notably winRAR) already do some lossless reencoding of JPEG.

> Luckily it might look like snow from a television set rather than a typical image produced by a camera.

That would actually compress rather well. A hard to compress random image would not look like TV snow (white dots, with space between them), but rather randomly colored dots that are continuous in the image.

Random link: "I am trying to design a cloth that, from the point of view of a camera, is very difficult to compress with JPG, resulting in big-size files (or leading to low image quality if file size is fixed)."

https://dsp.stackexchange.com/questions/2010/what-is-the-lea...

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