Robust structural similarity index measure for images with non-Gaussian distortions.

• Introduction of a new robust structure similarity index based on p th order statistics. • A simple and low cost special case: l1-norm based robust structure similarity index. • Extension of this measure to colour images. • Demonstration of the success of new index on various types of distortions. Structural Similarity Index Measure (SSIM) has been a very successful tool in image processing and computer vision. After almost two decades, it is still the most popular method for quantifying the similarity between original and distorted images with applications in image quality assessment, image retrieval, video coding, computer vision, image encryption and data-hiding. Despite its general success, there are some types of distortions, such as when the distortion is of non-Gaussian character, where SSIM does not sustain its success. We provide a generalized version of SSIM utilizing l p -norm (1 ≤ p ≤ 2) based sample moments as opposed to the classical SSIM which uses l 2 -norm based sample moments. In particular, we study the l 1 -norm special case and our simulations on well-known image-databases show superior performance compared to SSIM and related measures. [ABSTRACT FROM AUTHOR]