# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 2 Proposed Method
### Feature Extraction of Pictorial Regions
#### 2.3.2 Texture Features of Pictorial Regions in the Shearlet Domain  
For picture regions, besides the texture features in the spatial domain, the texture features in the shearlet domain are also employed as structural features in this study. In [14], the generalized local binary pattern (GLBP) operator is proposed to extract texture features from four subband images produced by the Laplacian of Gaussian filters and in the GLBP operator, the central pixel is compared with neighboring pixels by using a threshold. In [15], the wavelet local binary pattern operator is proposed to extract texture features from subbands generated by the wavelet transform. Inspired by these two pattern operators, in this paper, we propose a new texture descriptor called the shearlet local binary pattern (SLBP), which is used to extract texture features from the subbands generated by the shearlet transform. The extraction process of the texture features of pictorial regions in the shearlet domain is shown in Figure 6.  
Firstly, the discrete nonseparable shearlet transform (DNST) [46], is applied to pictorial regions of an SCI. The shearlet transform can mimic the multi-channel mechanism of the HVS and has some advantages over the wavelet transform. As the DNST can be regarded as a model of human vision, texture features in the shearlet domain are more discriminative in an IQA model. The formula of the DNST is given as  
\[B_{s,d}=\left\langle S_{P},\psi_{s,d}\right\rangle \tag{13}\]  
where \(B_{s,d}\) denotes the subband at the \(s\)th scale and the \(d\)th direction, \(s\) represents the scale index, \(d\) is the direction index, \(S_{P}\) stands for the gray values of pictorial regions of an SCI and \(\psi_{s,d}\) denotes the discrete nonseparable shearlet. In this study, the number of scales of the DNST is set to 4 and the numbers of directions in four scales are set to 8, 8, 4 and 4 from finer to coarser scales, respectively. Then, a total of 24 subbands are derived. Secondly, to extract texture features in the shearlet domain, the SLBP operator is applied to shearlet transform coefficients. Here, for each subband of the DNST, the proposed uniform and rotation invariant SLBP is defined as  
\[SLBP^{s,d}_{N_{C},R,T}(c)=\left\{ \begin{array}{cc}\sum\limits_{i=0}^{N_{C}-1 }g(B_{i}-B_{c}),&\text{if }U(B_{c})\leq 2\\ N_{C}+1,&\text{otherwise}\end{array} \right. \tag{14}\]  
Figure 7: The examples of texture features of pictorial regions in the spatial domain; **(a1–a8)** are the second-order LDP histograms along the direction \(0^{\circ}\) of pictorial regions contained in the corresponding subgraphs **(a1–a8)** of Figure 5.  
\[g(B_{i}-B_{c})=\left\{ \begin{array}{ll}1,&(B_{i}-B_{c})\geq T\\ 0,&(B_{i}-B_{c})<T\end{array} \right. \tag{15}\]  
\[U(B_{c})=\left|g\Big{(}B_{N_{C}-1}-B_{c}\Big{)}-g(B_{0}-B_{c})\right|+\sum_{i=1} ^{N_{C}-1}\left|g(B_{i}-B_{c})-g(B_{i-1}-B_{c})\right| \tag{16}\]  
where \(SLBP^{s,d}_{N_{C},R,T}(c)\) represents the SLBP value of the coefficient \(c\) in the subband at the \(s\)th scale and the \(d\)th direction; \(N_{C}\) denotes the quantity of the coefficients which are adjacent to the coefficient \(B_{c}\); \(R\) is the radius of the neighborhood of the coefficient \(c\); \(B_{c}\) and \(B_{i}\) stand for the intensity values of the central coefficient \(c\) and the \(i\)th adjacent coefficient, respectively; \(g\) denotes a binary function; \(T\) represents a threshold value; and \(U(B_{c})\) is the uniform pattern of the SLBP. Here, \(N_{C}\) and \(R\) are set to 4 and 1, respectively; \(T\) has three values, namely \(-2\), 0 and 5. Then, for each value of \(T\), \(SLBP^{s,d}_{A,R,T}(c)\) generates six results which vary from 0 to 5. Finally, the histogram of the SLBP is calculated as  
\[H^{SLBP}_{s,d,T,k}=\frac{1}{N_{S}}\sum_{c=1}^{N_{S}}f\Big{(}SLBP^{s,d}_{A,R,T}( c),k\Big{)} \tag{17}\]  
\[f\Big{(}SLBP^{s,d}_{A,R,T}(c),k\Big{)}=\left\{ \begin{array}{ll}1,&SLBP^{s,d}_ {A,R,T}(c)=k\\ 0,&\text{otherwise}\end{array} \right.