Abstract
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 $5\times 5$ -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.
Highlights
Quality assessment of an image is not a judgment of “beauty” or “realism,” but an objective measure [1] aiming to predict perceived image quality that is consistent with the human's subjective perception quality
This study shows that a limited number of edge profiles can be collected from natural images by the proposed smooth criterion described in IID, and the limited number of edge profiles can represent most edge maps of natural images
Considering that the human visual system is sensitive to the image's structure information, we investigated the spatial relationship of adjacent edge points, which is different from the existing image quality assessment (IQA) models based on structure information [2, 30] and the edge map [32,33,34]
Summary
Quality assessment of an image is not a judgment of “beauty” or “realism,” but an objective measure [1] aiming to predict perceived image quality that is consistent with the human's subjective perception quality. We classified the spatial distribution of adjacent edge points in terms of binary value into 65 groups of edge patterns, in which we excluded three groups representing pieces of straight lines with different directions which are not sensitive to quality evaluation for designing the general purpose IQA features. We employed a learned model based on the histogram feature to predict the image's perceptual quality, which shows a generalization ability across common distortion types. Considering that the human visual system is sensitive to the image's structure information, we investigated the spatial relationship of adjacent edge points, which is different from the existing IQA models based on structure information [2, 30] and the edge map [32,33,34].
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