Yi Ling

Evaluating Lossy Compression on Climate Data

While the amount of data used by today’s high-performance computing (HPC) codes is huge, HPC users have not broadly adopted data compression techniques, apparently because of a fear that compression will either unacceptably degrade data quality or that compression will be too slow to be worth the effort. In this paper, we examine the effects of three lossy compression methods (GRIB2 encoding, GRIB2 using JPEG 2000 and LZMA, and the commercial Samplify APAX algorithm) on decompressed data quality, compression ratio, and processing time. A careful evaluation of selected lossy and lossless compression methods is conducted, assessing their influence on data quality, storage requirements and performance. The differences between input and decoded datasets are described and compared for the GRIB2 and APAX compression methods. Performance is measured using the compressed file sizes and the time spent on compression and decompression. Test data consists both of 9 synthetic data exposing compression behavior and 123 climate variables output from a climate model. The benefits of lossy compression for HPC systems are described and are related to our findings on data quality.

Effects of fixed-rate CT projection data compression on perceived and measured CT image quality

Compression of computed tomography (CT) projection data reduces CT scanner bandwidth and storage costs. Since fixed-rate compression guarantees predictable bandwidth, fixed-rate compression is preferable to lossless compression, but fixed-rate compression can introduce image artifacts. This research demonstrates clinically acceptable image quality at 3:1 compression as judged by a radiologist and as estimated by an image quality metric called local structural similarity (SSIM). We examine other common, quantitative image quality metrics from image processing, including peak signal-to-noise (PSNR), contrast-to-noise ratio (CNR), and difference image statistics to quantify the magnitude and location of image artifacts caused by fixed-rate compression of CT projection data. Masking effects caused by local contrast, air and bone pixels, and image reconstruction effects at the images periphery and iso-center explain why artifacts introduced by compression are not noticed by radiologists. SSIM metrics in this study nearly always exceeds 0.98 (even at 4:1 compression ratios), which is considered visually indistinguishable. The excellent correlation of local SSIM and subjective image quality assessment confirms that fixed-rate 3:1 projection data compression on CT images does not affect clinical diagnosis and is rarely noticed. Local SSIM metrics can be used to significantly reduce the number of viewed images in medical image quality studies.