[daniel_rh]

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.