Even though the implementation details are far from trivial, the general idea is fairly typical. Most advanced compression algorithms work the same way.

- Using the previously decoded data, try to predict what's next, and the probability or being right

- Using an entropy coder, encode the difference between what is predicted and the actual data. The predicted probability will be used to define how many bits to assign to each possible value. The higher the probability, the less bits will be used for a "right" answer and the more bits will be used for a "wrong" answer.

Decoding works by "replaying" what the encoder did.

The most interesting part is the prediction. So much that some people think of compression as a better test for AIs that the Turing test. You are basically asking the computer to solve one of these sequence-based IQ tests.

And of course neural networks are one of the first thing we tend to think of when we want to implement an AI, and unsurprising it is not an uncommon approach for compression. For instance, the latest PAQ compressors use neural networks.

Of course, all that is a general idea. How to do it in practice is where the real challenge is. Here, the clever part is to "grow" the image from low to high resolution. Which kinds of reminds me of wavelet compression.