{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iflow-numerically-invertible-flows-for","title":"iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder","arxiv_id":"2111.00965","date":"2021-11-01","proceeding":"NeurIPS 2021 12","authors":["Shifeng Zhang","Ning Kang","Tom Ryder","Zhenguo Li"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2111.00965v1","url_pdf":"https://arxiv.org/pdf/2111.00965v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"image-compression","task_name":"Image Compression"}],"methods":[{"method_slug":"normalizing-flows","method_name":"Normalizing Flows"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-compression-on-imagenet32","task":"Image Compression","dataset":"ImageNet32","model":"iFlow","rank_in_archive_order":1,"of":5,"metrics":{"bpsp":"3.88"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2111.00965","atlas_url":"https://app.syntology.ai/?focus=2111.00965","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}