Combination of SSIM and JND with content-transition classification for image quality assessment

Abstract

Image quality assessment (IQA) is a crucial feature of many image processing algorithms. The state-of-the-art IQA index, the structural similarity (SSIM) index, has been able to accurately predict image quality by assuming that the human visual system (HVS) separates structural information from non-structural information in a scene. However, the precision of SSIM is relatively lacking when used to access blurred images. This paper proposes a novel metric of image quality assessment, the JND-SSIM, which adopts the just-noticeable difference (JND) algorithm to differentiate between plain, edge, and texture blocks and obtain a visibility threshold map. Based on varying block transition types between the reference and distorted image, SSIM values are assigned respective weights and scaled down by visibility threshold map. We then test our algorithm on the LIVE and TID Image Quality Database, thereby demonstrating that our improved IQA index is much closer to human opinion.