UNDERSTANDING AND SIMPLIFYING THE STRUCTURAL SIMILARITY METRIC

The structural similarity (SSIM) metric and its multi-scale extension (MS-SSIM) evaluate visual quality with a modified local measure of spatial correlation consisting of three components: mean, variance, and cross-correlation. This paper investigates how the SSIM components contribute to its quality evaluation of common image artifacts. The predictive performance of the individual components and pairwise component products is assessed using the LIVE image database. After a nonlinear mapping, the product of the variance and cross-correlation components yields nearly identical linear correlation with subjective ratings as the complete SSIM and MS-SSIM computations. A computationally simple alternative to SSIM (c.f. Eq. (6)) that ignores the mean component and sets the local average patch values to 128 exhibits a 1% decrease in linear correlation with subjective ratings to 0.934 from the complete SSIM evaluation with an over 20% reduction in the number of multiplications.