Image quality assessment based on edge

The research on image quality assessment (IQA) has been become a hot topic in most area concerning image processing. Seeking for the efficient IQA model with the neurophysiology support is naturally the goal people put the efforts to pursue. In this paper, we argue that comparing the edges position of reference and distorted image can well measure the image structural distortion and become an efficient IQA metric, while the edge is detected from the primitive structures of image convolving with LOG filters. The proposed metric is called NSER that has been designed following a simple logic based on the cosine distance of the primitive structures and two accessible improvements. Validation is taken by comparison of the well-known state-of-the-art IQA metrics: VIF, MS-SSIM, VSNR over the six IQA databases: LIVE, TID2008, MICT, IVC, A57, and CSIQ. Experiments show that NSER works stably across all the six databases and achieves the good performance.