Distributed Compression in the Era of Machine Learning: A Review of Recent Advances

Ezgi Ozyilkan, Elza Erkip

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

Abstract

Many applications from camera arrays to sensor networks require efficient compression and processing of correlated data, which in general is collected in a distributed fashion. While information-theoretic foundations of distributed compression are well investigated, the impact of theory in practice-oriented applications to this day has been somewhat limited. As the field of data compression is undergoing a transformation with the emergence of learning-based techniques, machine learning is becoming an important tool to reap the long-promised benefits of distributed compression. In this paper, we review the recent contributions in the broad area of learned distributed compression techniques for abstract sources and images. In particular, we discuss approaches that provide interpretable results operating close to information-theoretic bounds. We also highlight unresolved research challenges, aiming to inspire fresh interest and advancements in the field of learned distributed compression.

Original languageEnglish (US)
Title of host publication2024 58th Annual Conference on Information Sciences and Systems, CISS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350369298
DOIs
StatePublished - 2024
Event58th Annual Conference on Information Sciences and Systems, CISS 2024 - Princeton, United States
Duration: Mar 13 2024Mar 15 2024

Publication series

Name2024 58th Annual Conference on Information Sciences and Systems, CISS 2024

Conference

Conference58th Annual Conference on Information Sciences and Systems, CISS 2024
Country/TerritoryUnited States
CityPrinceton
Period3/13/243/15/24

Keywords

  • binning
  • Distributed source coding
  • learning
  • lossy compression
  • neural networks
  • rate-distortion theory
  • Wyner-Ziv coding

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Modeling and Simulation
  • Computational Theory and Mathematics

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