ANFIC: Image Compression Using Augmented Normalizing Flows
arXiv:2107.08470
Abstract
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.
References in corpus (7)
- Auto-Encoding Variational Bayes
- Pixel Recurrent Neural Networks
- Normalizing Flows: An Introduction and Review of Current Methods
- Joint Autoregressive and Hierarchical Priors for Learned Image Compression
- Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling
- Non-local Attention Optimized Deep Image Compression
- Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models