It was estimated that the world produced $59 ZB$ ($5.9 \times 10^{13} GB$) of
data in 2020, resulting in the enormous costs of both data storage and
transmission. Fortunately, recent advances in deep generative models have
spearheaded a new class of so-called "neural compression" algorithms, which
significantly outperform traditional codecs in terms of compression ratio.
Unfortunately, the application of neural compression garners little commercial
interest due to its limited bandwidth; therefore, developing highly efficient
frameworks is of critical practical importance. In this paper, we discuss
lossless compression using normalizing flows which have demonstrated a great
capacity for achieving high compression ratios. As such, we introduce iFlow, a
new method for achieving efficient lossless compression. We first propose
Modular Scale Transform (MST) and a novel family of numerically invertible flow
transformations based on MST. Then we introduce the Uniform Base Conversion
System (UBCS), a fast uniform-distribution codec incorporated into iFlow,
enabling efficient compression. iFlow achieves state-of-the-art compression
ratios and is $5\times$ quicker than other high-performance schemes.
Furthermore, the techniques presented in this paper can be used to accelerate
coding time for a broad class of flow-based algorithms.

本文介绍了使用归一化流实现高效无损压缩的新方法 iFlow, 该方法基于模块化缩放变换提出了一系列数值可逆流变换，同时引入了 Uniform Base Conversion System（UBCS）实现快速均匀分布编码，iFlow 实现了最先进的压缩比，并且比其他高性能方案快 5 倍。