Fixed-PSNR Lossy Compression for Scientific Data

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

doi:10.1109/CLUSTER.2018.00048

Fixed-PSNR Lossy Compression for Scientific Data

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

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 language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
Pages	314-318
Number of pages	5
ISBN (Electronic)	9781538683194
DOIs	https://doi.org/10.1109/CLUSTER.2018.00048
State	Published - Oct 29 2018
Event	2018 IEEE International Conference on Cluster Computing, CLUSTER 2018 - Belfast, United Kingdom Duration: Sep 10 2018 → Sep 13 2018

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
Volume	2018-September
ISSN (Print)	1552-5244

Conference

Conference	2018 IEEE International Conference on Cluster Computing, CLUSTER 2018
Country/Territory	United Kingdom
City	Belfast
Period	9/10/18 → 9/13/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Lossy compression
PSNR
Scientific data

ASJC Scopus subject areas

Software
Hardware and Architecture
Signal Processing

Access to Document

10.1109/CLUSTER.2018.00048

Cite this

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title = "Fixed-PSNR Lossy Compression for Scientific Data",

abstract = "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.",

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author = "Dingwen Tao and Sheng Di and Xin Liang and Zizhong Chen and Franck Cappello",

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year = "2018",

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doi = "10.1109/CLUSTER.2018.00048",

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Tao, D, Di, S, Liang, X, Chen, Z & Cappello, F 2018, Fixed-PSNR Lossy Compression for Scientific Data. in Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018., 8514891, Proceedings - IEEE International Conference on Cluster Computing, ICCC, vol. 2018-September, pp. 314-318, 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018, Belfast, United Kingdom, 9/10/18. https://doi.org/10.1109/CLUSTER.2018.00048

Fixed-PSNR Lossy Compression for Scientific Data. / Tao, Dingwen; Di, Sheng; Liang, Xin et al.
Proceedings - 2018 IEEE International Conference on Cluster Computing, CLUSTER 2018. 2018. p. 314-318 8514891 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2018-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Fixed-PSNR Lossy Compression for Scientific Data

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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