System-Aware Compression

Yehuda Dar, Michael Elad, Alfred M. Bruckstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Many information systems employ lossy compression as a crucial intermediate stage among other processing components. While the important distortion is defined by the system's input and output signals, the compression usually ignores the system structure, therefore, leading to an overall suboptimal rate-distortion performance. In this paper we propose a compression methodology for an operational rate-distortion optimization considering a known system layout, modeled using linear operators and noise. Using the alternating direction method of multipliers (ADMM) technique, we show that the design of the new globally-optimized compression reduces to a standard compression of a 'system adjusted' signal. Essentially, the proposed framework leverages standard compression techniques to address practical settings of the remote source coding problem. We further explain the main ideas of our method by theoretically studying the case of a cyclo-stationary Gaussian signal. We present experimental results for coding of one-dimensional signals and for video compression using the HEVC standard, showing significant gains by the adjustment to an acquisition-rendering system.

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Print)9781538647806
StatePublished - 15 Aug 2018
Externally publishedYes
Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
Duration: 17 Jun 201822 Jun 2018

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2018 IEEE International Symposium on Information Theory, ISIT 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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