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Oct 16, 2017 at 0:19 comment added MarcinKonowalczyk @StanleyPawlukiewicz I have figured it out. You're right, it's hard to do in general, but I found a way to do in in my particular case (see my answer)
Oct 16, 2017 at 0:16 vote accept MarcinKonowalczyk
Oct 16, 2017 at 0:16 answer added MarcinKonowalczyk timeline score: 3
Oct 12, 2017 at 16:44 comment added user28715 Information theory isn't about how to do things, it is about finding out if you can do things. You need to come up with a joint probability density that models the 2 images. One way of doing that would be a black box method like a neural net. If you can train a neural net to produce image 2, from image 1, and do that for many different pairs, that would seem to be equivalent to what you want. I don't think you can do that except for a very limited class of problems. Maybe, sequential frames in a video over a small time window.
Oct 12, 2017 at 16:31 comment added MarcinKonowalczyk @StanleyPawlukiewicz I am not entirely sure what you mean. Could you expand on your suggestion?
Oct 12, 2017 at 16:22 comment added user28715 I think you need to consider the mutual information between the 2 images, as a source and receiver.
Oct 12, 2017 at 0:23 comment added MBaz With no correlation, I don't see a way -- but if you succeed you'll have an awesome image compressor.
Oct 12, 2017 at 0:13 comment added MarcinKonowalczyk @MBaz You can assume none or, at leat, inconsistent. Imagine grayscale of 'lena' (a lady in a hat) and 'mandrill' (a close up face of a baboon).
Oct 12, 2017 at 0:10 comment added MBaz This is not my area, but I'd say that the answer will depend on the correlation between the two images. Could you explain if there is any?
Oct 12, 2017 at 0:01 review First posts
Oct 12, 2017 at 1:52
Oct 11, 2017 at 23:58 history asked MarcinKonowalczyk CC BY-SA 3.0