Compression for multiple reconstructions

Yehuda Dar, Michael Elad, Alfred M. Bruckstein

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

1 Scopus citations


In this work we propose a method for optimizing the lossy compression for a network of diverse reconstruction systems. We focus on adapting a standard image compression method to a set of candidate displays, presenting the decompressed signals to viewers. Each display is modeled as a linear operator applied after decompression, and its probability to serve a network user. We formulate a complicated operational ratedistortion optimization trading-off the network's expected mean-squared reconstruction error and the compression bit-cost. Using the alternating direction method of multipliers (ADMM) we develop an iterative procedure where the network structure is separated from the compression method, enabling the reliance on standard compression techniques. We present experimental results showing our method to be the best approach for adjusting high bit-rate image compression (using the state-of-the-art HEVC standard) to a set of displays modeled as blur degradations.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479970612
StatePublished - 29 Aug 2018
Externally publishedYes
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference25th IEEE International Conference on Image Processing, ICIP 2018


  • Alternating direction method of multipliers (ADMM)
  • Image compression
  • Image deblurring
  • Rate-distortion optimization
  • Signal compression

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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