Fixed-PSNR Lossy Compression for Scientific Data

Dingwen Tao, Sheng Di, Xin Liang, Zizhong Chen, Franck Cappello

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

14 Scopus citations


Error-controlled lossy compression has been studied for years because of extremely large volumes of data being produced by today's scientific simulations. None of existing lossy compressors, however, allow users to fix the peak signal-to-noise ratio (PSNR) during compression, although PSNR has been considered as one of the most significant indicators to assess compression quality. In this paper, we propose a novel technique providing a fixed-PSNR lossy compression for scientific data sets. We implement our proposed method based on the SZ lossy compression framework and release the code as an open-source toolkit. We evaluate our fixed-PSNR compressor on three realworld high-performance computing data sets. Experiments show that our solution has a high accuracy in controlling PSNR, with an average deviation of 0.1 ~ 5.0 dB on the tested data sets.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
Number of pages5
ISBN (Electronic)9781538683194
StatePublished - Oct 29 2018
Event2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 - Belfast, United Kingdom
Duration: Sep 10 2018Sep 13 2018

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)1552-5244


Conference2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2018 IEEE.


  • Lossy compression
  • PSNR
  • Scientific data

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Signal Processing


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