On a structural similarity index approach for floating-point data

Data visualization is typically a critical component of post-processing analysis workflows for floating-point output data from large simulation codes, such as global climate models. For example, images are often created from the raw data as a means for evaluation against a reference dataset or image. While the popular Structural Similarity Index Measure (SSIM) is a useful tool for such image comparisons, generating large numbers of images can be costly when simulation data volumes are substantial. In fact, computational cost considerations motivated our development of an alternative to the SSIM, which we refer to as the Data SSIM (DSSIM). The DSSIM is conceptually similar to the SSIM, but can be applied directly to the floating-point data as a means of assessing data quality. We present the DSSIM in the context of quantifying differences due to lossy compression on large volumes of simulation data from a popular climate model. Bypassing image creation results in a sizeable performance gain for this case study. In addition, we show that the DSSIM is useful in terms of avoiding plot-specific (but data-independent) choices that can affect the SSIM. While our work is motivated by and evaluated with climate model output data, the DSSIM may prove useful for other applications involving large volumes of simulation data.