Fixed-psnr lossy compression for scientific data
2018 IEEE International Conference on Cluster Computing (CLUSTER), 2018•ieeexplore.ieee.org
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 …
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 …
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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