Convergence analysis for image interpolation in terms of the cSSIM

TL;DR

This paper analyzes the convergence of the continuous SSIM (cSSIM) index in image interpolation, establishing theoretical bounds and rates of convergence, and validating findings through numerical experiments.

Contribution

It extends the cSSIM definition to include local weighting, proves its relation to the $L_2$ norm, and derives convergence rates for image interpolation methods.

Findings

cSSIM includes classical SSIM as a special case

Bounds on cSSIM can be derived from $L_2$ error bounds

Convergence rates for image interpolation methods are established

Abstract

Assessing the similarity of two images is a complex task that attracts significant efforts in the image processing community. The widely used Structural Similarity Index Measure (SSIM) addresses this problem by quantifying a perceptual structural similarity. In this paper we consider a recently introduced continuous SSIM (cSSIM), which allows one to analyze sequences of images of increasingly fine resolutions, and further extend the definition of the index to encompass the locally weighted version that is used in practice. For both the local and the global versions, we prove that the continuous index includes the classical SSIM as a special case, and we provide a precise connection between image similarity measured by the cSSIM and by the $L_2$ norm. Using this connection, we derive bounds on the cSSIM by means of bounds on the $L_2$ error, and we even prove that the two error measures are equivalent in certain circumstances. We exploit these results to obtain precise rates of convergence with respect to the cSSIM for several concrete image interpolation methods, and we further validate these findings by different numerical experiments. This newly established connection paves the way to obtain novel insights into the features and limitations of the SSIM, including on the effect of the local weighted window on the index performances.