  This paper introduces an end-to-end learned image compression system, termed
ANFIC, based on Augmented Normalizing Flows (ANF). ANF is a new type of flow
model, which stacks multiple variational autoencoders (VAE) for greater model
expressiveness. The VAE-based image compression has gone mainstream, showing
promising compression performance. Our work presents the first attempt to
leverage VAE-based compression in a flow-based framework. ANFIC advances
further compression efficiency by stacking and extending hierarchically
multiple VAE's. The invertibility of ANF, together with our training
strategies, enables ANFIC to support a wide range of quality levels without
changing the encoding and decoding networks. Extensive experimental results
show that in terms of PSNR-RGB, ANFIC performs comparably to or better than the
state-of-the-art learned image compression. Moreover, it performs close to VVC
intra coding, from low-rate compression up to nearly-lossless compression. In
particular, ANFIC achieves the state-of-the-art performance, when extended with
conditional convolution for variable rate compression with a single model.
