DeepZip: Lossless Data Compression Using Recurrent Neural Networks

Mohit Goyal, Kedar Tatwawadi, Shubham Chandak, Idoia Ochoa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sequential data is being generated at an unprecedented pace in various forms, including text and genomic data. This creates the need for efficient compression mechanisms to enable better storage, transmission and processing of such data. To solve this problem, many of the existing compressors attempt to learn models for the data and perform prediction-based compression. Since neural networks are known as universal function approximators with the capability to learn arbitrarily complex mappings, and in practice show excellent performance in prediction tasks, we explore and devise methods to compress sequential data using neural network predictors. We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets. The proposed compressor outperforms Gzip on the real datasets and achieves near-optimal compression for the synthetic datasets. The results also help understand why and where neural networks are good alternatives for traditional finite context models.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2019
Subtitle of host publication2019 Data Compression Conference
EditorsAli Bilgin, James A. Storer, Michael W. Marcellin, Joan Serra-Sagrista
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781728106571
DOIs
StatePublished - May 10 2019
Event2019 Data Compression Conference, DCC 2019 - Snowbird, United States
Duration: Mar 26 2019Mar 29 2019

Publication series

NameData Compression Conference Proceedings
Volume2019-March
ISSN (Print)1068-0314

Conference

Conference2019 Data Compression Conference, DCC 2019
CountryUnited States
CitySnowbird
Period3/26/193/29/19

Keywords

  • Data Compression
  • Deep Learning
  • Information Theory
  • Machine Learning

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Goyal, M., Tatwawadi, K., Chandak, S., & Ochoa, I. (2019). DeepZip: Lossless Data Compression Using Recurrent Neural Networks. In A. Bilgin, J. A. Storer, M. W. Marcellin, & J. Serra-Sagrista (Eds.), Proceedings - DCC 2019: 2019 Data Compression Conference [8712659] (Data Compression Conference Proceedings; Vol. 2019-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC.2019.00087