# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 2 Proposed Method
### Feature Extraction of Textual Regions  
In the proposed BSRSF metric, structural features of textual regions of an SCI are extracted from the values of multi-order derivatives. The framework of the feature extraction of textual regions is depicted in Figure 4.  
It is well known that a mass of characters exists in textual regions of SCIs and characters have diverse edges. Thus, textual regions of SCIs possess noticeable structural characteristics. The existing literature indicates that the multi-order derivatives can accurately describe the structural characteristics and the derivative information of different orders is closely correlated with different structural characteristics [40; 41]. The first-order derivative information is correlated with the slope and elasticity of a landscape, the second-order derivative information can represent the curvature of a landscape [40], and the higher-order derivative information can provide tiny distinguishing structural details of a landscape [41]. Thus, the derivative information of different orders can efficiently denote the structural changes of an image, which have an important effect on the perceptual distortion of SCIs. Further, since derivative values of different orders have different characteristics, they should be combined to supply more comprehensive structural information for IQA methods. To accurately depict the local structure of textual regions in SCIs, the magnitude and orientation of multi-order derivatives should be incorporated together. Therefore, in this paper, an improved histogram of oriented gradient (IHOG) descriptor is proposed to extract statistical features of the magnitude and orientation of multi-order derivatives. The histogram of oriented gradient (HOG) descriptor considers the statistical distribution of the gradient directions in a small patch of an image; meanwhile, the gradient magnitudes in this small patch are also incorporated into the HOG. The HOG descriptor was initially proposed to deal with the problem of human detection [42]. The underlying notion of the HOG descriptor is that the feature of the object shape in a small patch can be depicted accurately by the statistical distribution of the gradient values of this patch and the actual gradient values of this patch do not need to be known. Specifically, for the IHOG descriptor, the original gray values of textual regions are viewed as the zero-order derivative of textual regions and then the HOG descriptor of the zero-order derivative is calculated by employing the magnitude and orientation of the first-order derivative; the HOG descriptor of the first-order derivative is derived based on the magnitude and orientation of the second-order derivative; and similarly, the HOG descriptor of the \(n\)th-order derivative is calculated based on the magnitude and orientation of the \((n+1)\)th-order derivative. Firstly, in this paper, the Prewitt filter is adopted to calculate the multi-order derivatives since its computation is simple. The first-order derivative of textual regions of an SCI is calculated as  
\[d_{h}^{1}(x,y)=S_{T}(x,y)*P_{h},\;P_{h}=\frac{1}{3}\left[ \begin{array}{ccc}-1 &0&1\\ -1&0&1\\ -1&0&1\end{array} \right] \tag{1}\]  
Figure 4: The extraction of structural features of textual regions. \[d_{v}^{1}(x,y)=S_{T}(x,y)*P_{v},\ P_{v}=\frac{1}{3}\left[ \begin{array}{ccc}-1&-1&-1 \\ 0&0&0\\ 1&1&1\end{array} \right] \tag{2}\]  
where \(d_{h}^{1}(x,y)\) and \(d_{v}^{1}(x,y)\) denote the first-order derivation values of the horizontal and vertical orientations, respectively; \(S_{T}(x,y)\) is the gray values of textual regions of an SCI; the symbol \(*\) stands for the convolution operation; and \(P_{h}\) and \(P_{v}\) represent the Prewitt filters in the horizontal and vertical orientations, respectively.