Abstract: b'Deep learning based image compression has recently witnessed exciting\nprogress and in some cases even managed to surpass transform coding based\napproaches that have been established and refined over many decades. However,\nstate-of-the-art solutions for deep image compression typically employ\nautoencoders which map the input to a lower dimensional latent space and thus\nirreversibly discard information already before quantization. Due to that, they\ninherently limit the range of quality levels that can be covered. In contrast,\ntraditional approaches in image compression allow for a larger range of quality\nlevels. Interestingly, they employ an invertible transformation before\nperforming the quantization step which explicitly discards information.\nInspired by this, we propose a deep image compression method that is able to go\nfrom low bit-rates to near lossless quality by leveraging normalizing flows to\nlearn a bijective mapping from the image space to a latent representation. In\naddition to this, we demonstrate further advantages unique to our solution,\nsuch as the ability to maintain constant quality results'