# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 1 Introduction  
Recently, screen content images (SCIs) have been widely applied as a form of information representation in our modern society owing to the popularization of multimedia applications including remote screen sharing, Cloud and mobile computing, commodity advertisements of online shopping websites and real-time online teaching [1; 2]. In many actual engineering applications, including compression, storage, transmission and display, the visual quality of SCIs will inevitably be degraded owing to distortions including noise, blur, contrast variation, blockiness and quantization loss. Undoubtedly, the quality degradation of SCIs will significantly affect the visual perception of observers. Thus, it is necessary and meaningful to develop quality evaluation methods for SCIs in actual engineering applications. Over recent decades, a large number of image quality assessment (IQA) methods have been elaborately designed and applied in the field of digital image processing. The peak signal-to-noise ratio (PSNR) is a conventional IQA method and has been applied extensively. However, it has inferior prediction performance since it only deals with the difference between pixels and doesnot take into account the perceptual properties of human vision. To overcome this drawback, the research community has proposed many advanced full-reference (FR) IQA metrics that require the entire information of the reference image. These metrics skillfully model intrinsic properties of the human visual system (HVS) and representative metrics include structure similarity (SSIM) [3], feature similarity [4], visual information fidelity [5], gradient magnitude similarity deviation (GMSD) [6], the internal generative mechanism (IGM) metric [7] and deep similarity [8]. In [3], the quality of an image is measured by combining the changes from the luminance, contrast and structure. In [4], two complementary low-level features, namely the phase congruency and the image gradient magnitude, are adopted to characterize the image local quality. In [5], the loss of image information is quantified and used to assess the visual quality of an image. In [6], the standard deviation of the gradient magnitude similarity map is calculated as the quality index of an image. In [7], according to the IGM theory, an autoregressive prediction method is used to decompose an image into the predicted and disorderly parts whose distortions are measured by the structural similarity and the PSNR, respectively. In [8], the local similarities of features generated by the convolutional neural network (CNN) are calculated and pooled together to assess the quality of an image. Additionally, alongside the FR IQA metrics, some reduced-reference (RR) IQA metrics [9], and no-reference/blind IQA metrics [10], have also been presented over recent decades. The RR IQA metrics only need partial information of the reference image, while the no-reference (NR) IQA metrics need no information from the reference image. Many blind IQA methods first extract quality-aware features and then these features are supplied into a machine learning model to obtain the quality assessment result. [PERSON] et al. [11], presented a blind IQA metric called BRISQUE in which the naturalness of an image is quantified and natural scene statistics (NSS) features of locally normalized luminance values are adopted. [PERSON] et al. [12], presented a blind IQA metric which adopts two types of features, namely the luminance features represented by the luminance histogram and the structural features denoted by the histogram of the local binary pattern (LBP) of the normalized luminance. [PERSON] et al. [13], designed a blind IQA metric based on structural features denoted by the gradient-weighted histogram of the LBP computed from gradient values. In [14; 15], the statistical histograms of the texture information of an image are extracted as quality-aware features to describe the distortion degree of the image.