Analyzing the Impact of Lossy Data Reduction on Volume Rendering of Cosmology Data

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

Cosmology simulations are among some of the largest simulations currently run on supercomputers, generating terabytes to petabytes of data for each run. Consequently, scien-tists are seeking to reduce the amount of storage needed while preserving enough quality for analysis and visualization of the data. One of the most commonly used visualization techniques for cosmology simulations is volume rendering. Here, we investigate how different types of lossy error-bound compression algorithms affect the quality of volume-rendered images generated from reconstructed datasets. We also compute a number of image quality assessment metrics to determine which ones are the most effective at identifying artifacts in the visualizations.

Identifier

85147794220 (Scopus)

ISBN

[9781665463379]

Publication Title

Proceedings of Drbsd 8 2022 8th International Workshop on Data Analysis and Reduction for Big Scientific Data Held in Conjunction with Sc 2022 the International Conference for High Performance Computing Networking Storage and Analysis

External Full Text Location

https://doi.org/10.1109/DRBSD56682.2022.00007

First Page

11

Last Page

20

Grant

89233218CNA000001

Fund Ref

U.S. Department of Energy

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