  Data visualization is a critical component in terms of interacting with
floating-point output data from large model simulation codes. Indeed,
postprocessing analysis workflows on simulation data often generate a large
number of images from the raw data, many of which are then compared to each
other or to specified reference images. In this image-comparison scenario,
image quality assessment (IQA) measures are quite useful, and the Structural
Similarity Index (SSIM) continues to be a popular choice. However, generating
large numbers of images can be costly, and plot-specific (but data independent)
choices can affect the SSIM value. A natural question is whether we can apply
the SSIM directly to the floating-point simulation data and obtain an
indication of whether differences in the data are likely to impact a visual
assessment, effectively bypassing the creation of a specific set of images from
the data. To this end, we propose an alternative to the popular SSIM that can
be applied directly to the floating point data, which we refer to as the Data
SSIM (DSSIM). While we demonstrate the usefulness of the DSSIM in the context
of evaluating differences due to lossy compression on large volumes of
simulation data from a popular climate model, the DSSIM may prove useful for
many other applications involving simulation or image data.
