# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 1 Introduction
[26], proposed an RR quality assessment method of compressed SCIs in which wavelet domain features including the mean, variance and entropy of wavelet coefficients are used to learn a regression model. [PERSON] et al. [27], presented an RR SCIQA method based on feature points identified by the cascade DOG filters. The aforementioned methods of SCIs [21; 22; 23; 24; 25; 26; 27] have one common drawback: they employ the same feature representation method to characterize the quality degradation of the entire content of SCIs and do not take different steps to deal with the different contents of SCIs. Since human eyes have an obviously different visual experience to the distortions of the textual and pictorial contents contained in SCIs, it is unreasonable to employ the same features to denote the quality degradation of the textual and pictorial content of SCIs. Additionally, these FR or RR methods require the entire or partial information of reference SCIs which cannot be acquired in the majority of actual cases. [PERSON] et al. [28], put forward an NR SCIQA model in which one free energy feature and twelve structural degradation features are extracted to train the assessment model. [PERSON] et al. [29], designed a blind SCIQA method based on the CNN, in which both the predicted and unpredicted parts obtained according to the IGM theory are inputted into the CNN. However, in [28; 29], predictive values generated  
Figure 1: Two typical examples of the screen content images (SCIs) in the SIQAD database. (**a**) “cim 1” and (**b**) “cim19”. by objective FR SCIQA methods rather than subjective ratings values are used as training labels, which may result in a deviation. In [30], local and global sparse representations are conducted to design an NR SCIQA model. [PERSON] et al. [31], performed the blind quality assessment of SCIs based on statistical orientation features and structural features denoted by the LBP histograms of nine gradient maps. [PERSON] et al. [32], proposed an NR quality evaluation method of compressed SCIs in which the features of corners and edges at multiple scales are integrated by using a multi-scale weighting strategy. [PERSON] et al. [33], presented a blind SCIQA model by considering both local features denoted by the histograms of locally normalized luminance values and global features denoted by the histograms of the texture features extracted from gradient maps. [PERSON] et al. [17], developed a blind assessment model of SCIs comprising four elements, namely picture complexity, screen content statistics, brightness and sharpness. Although these existing blind evaluation models, which were specifically developed for SCIs, obtain better prediction performance compared to traditional evaluation models of natural images, they still cannot obtain a high prediction accuracy and there is still a great deal of room to enhance their performances. Thus, the blind quality assessment of SCIs remains a challenging problem and needs to be further investigated in depth by the research community. To further improve the predictive accuracy of existing blind evaluation methods of SCIs, in this study, we propose a blind SCIQA method based on regionalized structural features (BSRSF) which are closely relevant to the intrinsic quality of SCIs. Firstly, considering very different characteristics of the textual and pictorial content in an SCI, the SCI is segmented into two completely different types: textual regions and pictorial regions. Secondly, to derive respective assessment values of textual and pictorial regions, their features are respectively extracted by applying different methods according to their characteristics and then they are separately supplied to machine learning models, i.e., support vector regression (SVR). Specifically, given the noticeable structural information contained in textual regions, the structural information is used as the quality-aware feature of textual regions.