iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder

By Shifeng Zhang, Ning Kang, Tom Ryder, Zhenguo Li. 2021

46 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Zhu
2. Chongxuan Li
3. Zhihao Duan
4. Feng Zhu
5. Jackie Ma
6. Zhan Ma
7. Siyu Wang
8. Jianfei Chen

In Computer Vision and Pattern Recognition

1. Jun Zhu
2. Chongxuan Li
3. Jianfei Chen
4. Zhihao Duan
5. Feng Zhu
6. Jackie Ma
7. Zhan Ma
8. Siyu Wang

Citations in the corpus

• Fast Lossless Neural Compression with Integer-Only Discrete Flows (2022, methodology): “LBB iVPF iFlow IDF IODF (Zhang et al., 2021c;b).”
• QARV: Quantization-Aware ResNet VAE for Lossy Image Compression (2023, background): “For example, recent studies have shown that many powerful generative models, such as auto-regressive models [1], Variational Autoencoders [2], Normalizing Flows [3], and diffusion probabilistic models [4], can also serve as highly effective lossless compressors.”