Fixed-PSNR Lossy Compression for Scientific Data

TL;DR

This paper introduces a novel fixed-PSNR lossy compression method for scientific data, enabling precise control over compression quality, which is implemented within the SZ framework and validated on real HPC datasets.

Contribution

It presents the first fixed-PSNR lossy compression technique for scientific data, enhancing quality control in data reduction workflows.

Findings

High accuracy in PSNR control with deviation of 0.1~5.0 dB

Implemented as open-source within SZ framework

Validated on three real-world HPC datasets

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 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.