edu.ucar.opensky::technotes:590 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T23:03:23.129062 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7gh9nd9 A Statistical Approach to Obtaining a Data Structural Similarity Index Cutoff Threshold 2021-09-28T00:00:00Z publication https://n2t.org/ark:/85065/d7gh9nd9 https://doi.org/10.5065/jc2r-5289 Pinard, A. author Baker, Allison author Hammerling, D. author UCAR/NCAR - Library publisher Huge climate simulations such as the Community Earth System Model Large Ensemble (CESM-LE) output massive amounts of data, and switching from lossless to lossy compression of this data is inevitable. Applying lossy compression to climate data requires a guarantee that scientific conclusions will not be affected by the compression process. One way to check if a scientist's conclusions will be altered is by assessing if the data has been visually altered in any way. To that extent, the Data Structural Similarity Index Measure (DSSIM) is designed to test the visual similarity between two images. It is based on the well-known Structural Similarity Index Measure (SSIM), but has been modified to fit our application. The DSSIM is computed on two datasets instead of two images. This makes the measure invariant to the plotting parameters. Additionally, the DSSIM is much more efficient to compute on a floating-point dataset than the SSIM. A user study in a previous work determined an appropriate SSIM threshold, above which images are highly likely to be visually indistinguishable. In this work, we use the results of the previous study and statistical techniques to translate the SSIM threshold to an appropriate cutoff threshold for the DSSIM. We find the appropriate threshold by minimizing the pass/fail classification difference between images classified using the DSSIM and the previously acquired SSIM threshold. This threshold results in agreement between the image classification results using either metric in more than 92% of cases. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright Author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation