No-Reference Image Quality Assessment Based on Edge Pattern feature in the Spatial Domain

This paper proposes a general-purpose no-reference image quality assessment (NR-IQA) method that investigates the image’s structure information from a new aspect, i.e., the characteristic of image edge profiles that depict the directional property of adjacent edge points in the spatial domain of the image. More specifically, we extracted the image’s edge map based on Laplacian of Gaussian (LoG) filtration and zero-crossing (ZC) detection and refined the edge map to be 1-pixel wide. We then explored the edge map by investigating edge profiles’ statistics in a local window with a 5×5-pixel size. Considering the consensus that natural images consist of directional structures, we found that the spatial distribution property of adjacent edge points can be represented through several edge profiles called edge patterns, which are selected from natural images with a proposed smooth criterion. With the proposed edge patterns and their statistical histogram for the image and the support vector regression technique, we proposed the NR-IQA model based on the edge patterns in the spatial domain, named EPISD. The proposed method has been extensively validated on the LIVE, CSIQ, TID2013, MDID2017, SIQAD, and SCID databases. The experimental results showed that EPISD has a competitive performance with state-of-the-art methods and works stably across different databases.