  Deep learning based image compression has recently witnessed exciting
progress and in some cases even managed to surpass transform coding based
approaches that have been established and refined over many decades. However,
state-of-the-art solutions for deep image compression typically employ
autoencoders which map the input to a lower dimensional latent space and thus
irreversibly discard information already before quantization. Due to that, they
inherently limit the range of quality levels that can be covered. In contrast,
traditional approaches in image compression allow for a larger range of quality
levels. Interestingly, they employ an invertible transformation before
performing the quantization step which explicitly discards information.
Inspired by this, we propose a deep image compression method that is able to go
from low bit-rates to near lossless quality by leveraging normalizing flows to
learn a bijective mapping from the image space to a latent representation. In
addition to this, we demonstrate further advantages unique to our solution,
such as the ability to maintain constant quality results through re-encoding,
even when performed multiple times. To the best of our knowledge, this is the
first work to explore the opportunities for leveraging normalizing flows for
lossy image compression.
