A comprehensive assessment of the structural similarity index

In recent years the structural similarity index has become an accepted standard among image quality metrics. Made up of three components, this technique assesses the visual impact of changes in image luminance, contrast, and structure. Applications of the index include image enhancement, video quality monitoring, and image encoding. As its status continues to rise, however, so do questions about its performance. In this paper, it is shown, both empirically and analytically, that the index is directly related to the conventional, and often unreliable, mean squared error. In the first evaluation, the two metrics are statistically compared with one another. Then, in the second, a pair of functions that algebraically connects the two is derived. These results suggest a much closer relationship between the structural similarity index and mean squared error.