A Novel Image Structural Similarity Index Considering Image Content Detectability Using Maximally Stable Extremal Region Descriptor

Abstract

The image content detectability and image structure preservation are closely related concepts with undeniable role in image quality assessment. However, the most attention of image quality studies has been paid to image structure evaluation, few of them focused on image content detectability. Examining the image structure was firstly introduced and assessed in Structural SIMilarity (SSIM) measure, in which, the definition of image structure is constrained to the intensity covariance between the reference and test images. Indeed, this measure discerns the luminance changes in the pixels of the reference and test images, by employing the low-level statistical features. But this minimal definition of image structure does not cover the issue of image content detectability. In this study, we found that the status of image region smoothness can reflect its structural content. So, we proposed a novel smoothness measure based on the maximally stable extremal regions (MSER) descriptor. Subsequently, we proposed a novel image structural similarity measure, in which the fidelity of image region smoothness is also taken into account. Experimental results on five popular benchmark image databases, include A57, LIVE, CSIQ, TID2008 and TID2013, are provided, which confirm that the proposed approach has a reasonable prediction performance compared to the state-of-the-art image quality metrics.