# Fixed-PSNR Lossy Compression for Scientific Data

arXiv: https://arxiv.org/abs/1805.07384 · HTML: https://mlsanity.com/paper/1805.07384/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 2 of 2)

- [Exploring Autoencoder-based Error-bounded Compression for Scientific Data](https://mlsanity.com/paper/2105.11730/) (2021)
- [SIMD Lossy Compression for Scientific Data](https://mlsanity.com/paper/2201.04614/) (2022)

## Abstract (arXiv)

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.