Most learning-based image compression methods lack efficiency for high image
quality due to their non-invertible design. The decoding function of the
frequently applied compressive autoencoder architecture is only an approximated
inverse of the encoding transform. This issue can be resolved by using
invertible latent variable models, which allow a perfect reconstruction if no
quantization is performed. Furthermore, many traditional image and video coders
apply dynamic block partitioning to vary the compression of certain image
regions depending on their content. Inspired by this approach, hierarchical
latent spaces have been applied to learning-based compression networks. In this
paper, we present a novel concept, which adapts the hierarchical latent space
for augmented normalizing flows, an invertible latent variable model. Our best
performing model achieved average rate savings of more than 7% over comparable
single-scale models.

本文旨在提出一种创新的概念，将分层潜空间应用于增强式归一化流，建立可逆的潜变量模型，以解决许多学习型图像压缩方法的非可逆性问题。经过实验表明，本模型相较于单尺度模型，平均压缩率提高了 7% 以上。

图像压缩的多尺度增强归一化流

Multiscale Augmented Normalizing Flows for Image Compression

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.

本文介绍了基于增强标准流模型（ANF）的端到端学习图像压缩系统 ANFIC，该模型通过层叠多个变分自编码器（VAE）来提高模型表达能力，实现了在流模型框架下的 VAE 压缩，支持多种质量级别的压缩，与现有最先进技术相比，在 PSNR-RGB 方面表现相当或更好，在单一模型下实现了可变速率压缩的条件卷积，达到了最先进的性能。