  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 real-world
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.
