ANFIC: Image Compression Using Augmented Normalizing Flows

Yung Han Ho, Chih Chun Chan, Wen-Hsiao Peng, Hsueh-Ming Hang, Marek Domański

Research output: Contribution to journalArticlepeer-review


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 perceptually 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. The source code of ANFIC can be found at .
Original languageAmerican English
Pages (from-to)613 - 626
JournalIEEE Open Journal of Circuits and Systems
Issue number2644-1225
StatePublished - Nov 2021


  • Learning-based image compression
  • , flow-based image compression
  • augmented normalizing flows
  • , perceptually lossless image compression,
  • variable rate image compression


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