# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 1 Introduction
In [16], NSS features extracted from reference images are used to learn a multivariate Gaussian model and then this learned model is used to evaluate the quality of distorted images. Although the IQA methods mentioned above obtain superior performance, they have been specially developed to predict the quality of natural images and cannot be used to precisely assess the quality of SCIs. The reason for this is that SCIs have some distinctly different characteristics compared to natural images. Firstly, their contents are different. Generally, texts, natural images, slides and logos are mixed in an SCI and so an SCI has rough edges, simple shapes, thin lines and a small number of colors. Two typical examples of SCIs are shown in Figure 1. However, a natural image contains continuous-tone content with slow-varying edges, complicated structures, thick lines and more colors. Secondly, their statistical distributions are different. In general, after luminance values of a natural image are processed by the mean subtracted contrast normalized (MSCN) operation, their statistical distribution can be modeled by a Gaussian function [11]. By comparison, for an SCI, this statistical distribution behaves like a Laplacian contour [17] and the curve of this statistical distribution varies dramatically. Specifically, the center of this curve has a keen-edged pimpling and the remaining parts are still wavy [18]. Thirdly, their image activity levels [19], are different. Because the pixel values of an SCI have greater variations in local regions, the activity measurement value of an SCI is greater than that of a natural image [18]. As SCIs and natural images have these different properties, users have completely different viewing experiences regarding the quality degradation of SCIs and natural images. Therefore, the existing IQA methods developed for natural images are inappropriate to assess the quality of SCIs. To date, a few algorithms have been proposed to perform the quality evaluation of SCIs. The earliest study of the quality assessment of SCIs was conducted by [PERSON] et al. [18], who proposed an FR screen content image quality assessment (SCIQA) method called SPQA. In this method, for textual layers of SCIs, both luminance and sharpness similarities are calculated, while for pictorial layers of SCIs, only the sharpness similarity is computed. Respective quality values of textual and pictorial layers are combined as the overall quality score of a distorted SCI by employing a weighting activity map. However, the predictive performance of the SPQA method needs to be improved further. [PERSON] et al. [20], proposed an FR SCIQA method, in which the similarity of structural features denoted by the gradient information is calculated to estimate the quality of textual regions of the SCI and the similarities of luminance features and structural features denoted by the LBP features are computed to predict the quality of pictorial regions of the SCI. [PERSON] et al. [21], explored the edge variation of SCIs in depth and employed three edge characteristics including the contrast, width and direction of edges, which are extracted from a parametric edge model. [PERSON] et al. [22], adopted a two-scale difference-of-Gaussian (DOG) filter to extract the edges of an SCI and the similarities of small-scale edges are calculated and combined by using larger-scale edges as weighting values. [PERSON] et al. [23], designed an FR SCIQA method based on edge characteristics extracted from gradient values, which include the edge sharpness, the edge brightness change, the edge contrast change and the edge chrominance. In [24], the local similarities of two chrominance components and Gabor features generated by the imaginary part of the Gabor filer are computed and combined to produce the assessment score. In [25], statistical features of the primary visual and uncertainty information are used to design an RR SCIQA metric. [PERSON] et al.