A Statistical Approach to Obtaining a Data Structural Similarity Index Cutoff Threshold

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.