Lossy and lossless are way more transferable than people give credit. Long winded explanation as best as i can in a HN comment. Essentially for state of the ar

Lossy and lossless are way more transferable than people give credit. Long winded explanation as best as i can in a HN comment. Essentially for state of the art compression both the encoder and the decoder have the same algorithm. They look at the bits encoded/decoded so far, they both run exactly the same prediction on those bits seen so far using some model that predicts based on past data (AI is fantastic for this). If the prediction was 99% likely that the next bit is a '1' the encoder only writes a fraction of a bit to represent that (assuming the prediction is correct) and on the other side the decoder will have the same prediction at that point and either read the next large number of bits to correct or it will be able to simple write '1' to the output and start on the prediction of the next bit given that now written '1'. Essentially lossy predictions of the next data are great tools to losslessly compress data as those predictions of the next bit/byte/word minimize the data needed to losslessly encode that next bit/byte/word. Likewise you can trivially make a lossy compressor out of a lossless one. Lossy and lossless just aren't that different. The longstanding Hutter prize for AI in fact judges the AI on how well it can compress data. http://prize.hutter1.net/ http://prize.hutter1.net/ This is based in the fact that what we think of as AI and compression are quite interchangeable. There's a whole bunch of papers out on this. http://prize.hutter1.net/hfaq.htm#compai http://prize.hutter1.net/hfaq.htm#compai I have nothing to do with Hutter but i know all about AI and data compression and their relation.