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Poster
iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder
Shifeng Zhang · Ning Kang · Tom Ryder · Zhenguo Li

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
in Poster Session 5 »
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.
Keywords: Generative Model

Author Information

Shifeng Zhang (Department of Computer Science and Technology, Tsinghua University)
Ning Kang (The University of Hong Kong)
Tom Ryder (Huawei Technologies Ltd.)
Zhenguo Li (Noah's Ark Lab, Huawei Tech Investment Co Ltd)

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