Computer Graphics Laboratory ETH Zurich


Lossy Image Compression with Normalizing Flows

L. Helminger, A. Djelouah, M. Gross, C. Schroers

Neural Compression Workshop @ ICLR (2021)


Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches.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 ir-reversibly discard information already before quantization. In contrast, traditional approaches in image compression employ an invertible transformation before per-forming the quantization step. In this work, we propose a deep image compression method that is similarly 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. 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 firstwork leveraging normalizing flows for lossy image compression


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