# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 2 Proposed Method
### Feature Extraction of Pictorial Regions
#### 2.3.1 Texture Features of Pictorial Regions in the Spatial Domain  
Human vision is very sensitive to the texture variation of an image and so the texture feature should be considered adequately in an IQA model. Generally, the LBP, which can encode the pristine microstructures of an image, is used as the local texture descriptor of an image [43]. However, the LBP has two evident drawbacks: first, in the coding principle of the LBP, the code of a pixel does not consider the directional information of local image structures; second, the LBP is only the first-order derivative pattern and it does not contain the more detailed discriminative information from high-order derivatives. Thus, the application of the LBP in the IQA model will result in comparatively poor predictive performance. To overcome these two drawbacks, [PERSON] et al. [41], presented the local derivative pattern (LDP), which can describe the local structural primitives of an image by extracting more detailed texture features from high-order derivatives in four directions. In [44; 45], the LDP is adopted to construct the FR IQA model. Inspired by the above literature, in the proposed NRBSF method, the LDP is introduced to extract the discriminative texture features of pictorial regions  
Figure 6: The process of the feature extraction of pictorial regions. in the spatial domain. The detailed extraction process of texture features in the spatial domain is illustrated in Figure 6.  
The formula of the LDP is defined as  
\[LDP_{\theta}^{n}(p)=\sum_{i=0}^{N_{A}-1}f\Big{(}G_{\theta}^{n-1}(p)\times G_{ \theta}^{n-1}(p_{i})\Big{)}2^{i} \tag{8}\]  
\[f\Big{(}G_{\theta}^{n-1}(p)\times G_{\theta}^{n-1}(p_{i})\Big{)}=\left\{ \begin{array} []{c}0,\ G_{\theta}^{n-1}(p)\times G_{\theta}^{n-1}(p_{i})\geq 0\\ 1,\ G_{\theta}^{n-1}(p)\times G_{\theta}^{n-1}(p_{i})<0\end{array} \right. \tag{9}\]  
where \(LDP_{\theta}^{n}(p)\) denotes the \(n\)th-order LDP value of the pixel \(p\) along the direction \(\theta\) whose values include \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\) and \(135^{\circ}\); \(N_{A}\) represents the number of pixels which are adjacent to the pixel \(p\); \(G_{\theta}^{n-1}(p)\) and \(G_{\theta}^{n-1}(p_{i})\) stands for \((n-1)\)th-order derivative values of the pixel \(p\) and the \(i\)th pixel \(p_{i}\) which is adjacent to the pixel \(p\), respectively; and \(f\) is a binary function. In (8), the \(n\)th-order LDP is coded by using \((n-1)\)th-order derivative values. Additionally, the LBP can be considered to be a form of the first-order derivative of the LDP. Here, the statistical distributions of the LDP, namely the histograms of the LDP, are used as feature descriptors. After calculating the LDP code of each pixel of pictorial regions, we calculate the occurrence histograms of the LDP as follows:  
\[H_{n,\theta,k}^{LDP}=\frac{1}{N_{P}}\sum_{p=1}^{N_{P}}f\Big{(}LDP_{\theta}^{n} (p),B(k)\Big{)} \tag{10}\]  
\[f\Big{(}LDP_{\theta}^{n}(p),B(k)\Big{)}=\left\{ \begin{array}{c}1,\ LDP_{ \theta}^{n}(p)\in B(k)\\ 0,\ \text{otherwise}\end{array} \right. \tag{11}\]  
where \(H_{n,\theta,k}^{LDP}\) denotes the value of the \(k\)th bin in the histogram of the \(n\)th derivative along the direction \(\theta\); \(k\) stands for the bin index of this histogram and its value varies from 1 to 10; \(n\) is the derivative index and its value includes 1, 2 and 3; \(N_{P}\) denotes the number of the total pixels in pictorial regions; and \(B(k)\) represents the interval between two adjacent bins in the histogram. When \(n\) is equal to 1, \(\theta\) is meaningless and so \(H_{n,\theta,k}^{LDP}\) is changed into \(H_{1,k}^{LDP}\). For each order of LDP along one direction, one histogram with 10 bins can be generated and these bins of this histogram are used as structural features. In view of both computational complexity and accuracy, the first three orders of local derivative patterns (LDPs), namely the first-, second- and third-order LDPs, are adopted in the proposed BSRSF method.