Fixed-PSNR Lossy Compression for Scientific Data
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.
D. Tao et al., "Fixed-PSNR Lossy Compression for Scientific Data," Proceedings of the IEEE International Conference on Cluster Computing, ICCC, pp. 314-318, Institute of Electrical and Electronics Engineers (IEEE), Oct 2018.
The definitive version is available at https://doi.org/10.1109/CLUSTER.2018.00048
2018 IEEE International Conference on Cluster Computing, ICCC (2018: Sep. 10-13, Belfast, UK)
Keywords and Phrases
Lossy Compression; PSNR; Scientific Data
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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29 Oct 2018