# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 2 Proposed Method
### Feature Extraction of Pictorial Regions
#### 2.3.3. Luminance Features of Pictorial Regions  
Besides the texture information of an image, human vision also has high sensitivity to the luminance variation of an image which can induce obvious distortions and so the luminance features also have a high correlation with the perceptual quality of an image. In this study, the luminance information is used as the complementary feature of pictorial regions. In [11], the distribution of MSCN values is modeled approximately by the generalized Gaussian function (GGF), the distributions of pairwise products of the neighboring MSCN values in four directions are modeled approximately by the asymmetric generalized Gaussian function (AGGF) and 18 parameters of GGF and AGGF are adopted as luminance features of the image. However, this feature representation method has a drawback that fitting errors will inevitably be produced by this approximate modeling method. To overcome this drawback, in this paper, the statistical histogram is adopted as the representation form of the luminance information since the histogram does not produce fitting errors. The calculation process of luminance histograms is illustrated in Figure 6. To be specific, the histograms of MSCN values and four pairwise products of neighboring MSCN values are used as the luminance features of pictorial regions. Additionally, we also consider the statistical information from the local average values and local contrast values of pictorial regions. Local average values can mimic the point spread function of the optics in human eyes and local contrast values have a close relationship with the image quality. Likewise, their histograms are calculated as luminance features. The MSCN operation is conducted as follows:  
\[S_{P}^{M}(x,y)=\frac{S_{P}(x,y)-S_{P}^{A}(x,y)}{S_{P}^{\alpha}(x,y)+c} \tag{20}\]  
where \(S_{P}^{M}(x,y)\) denotes the MSCN value; \(S_{P}(x,\ y)\) represents the gray values of pictorial regions; \(S  
For MSCN values, four pairwise products, local average values and local contrast values, their absolute values are calculated first and then their statistical histograms are computed as  
\[H_{W,k}^{L}=\frac{1}{N_{P}}\sum_{(x,y)}f\Big{(}\big{|}S_{P}^{W}(x,y)\big{|},B(k) \Big{)} \tag{27}\]  
\[f\Big{(}\big{|}S_{P}^{W}(x,y)\big{|},B(k)\Big{)}=\left\{ \begin{array}{ll}1,& \big{|}S_{P}^{W}(x,y)\big{|}\in B(k)\\ 0,&\text{otherwise}\end{array} \right. \tag{28}\]  
where \(H_{W,k}^{L}\) denotes the value of the \(k\)th bin of the histogram; \(k\) stands for the bin index of the histogram and its value varies from 1 to 10; \(N_{P}\) represents the number of pixels in pictorial regions; \(B(k)\) is the interval between two neighboring bins in the histogram; and \(W\) denotes the type index of the histograms. The value of \(W\) includes \(M\), \(A\), \(\sigma\), \(H\), \(v\), \(D_{1}\) and \(D_{2}\). \(M\), \(A\) and \(\sigma\) stand for MSCN values, local average values and local contrast values, respectively. \(H\), \(v\), \(D_{1}\) and \(D_{2}\) represent four pairwise products of MSCN values. In this way, each histogram generates 10 bins which are employed as luminance features. Figure 9 illustrates the examples of luminance features of pictorial regions. Each subgraph of (a1-a8) of Figure 9 contains the histogram of MSCN values of pictorial regions contained in distorted SCIs caused by different distortion types. From Figure 9, we can observe that different histograms of MSCN values are produced for pictorial regions of distorted SCIs with different distortion types. Thus, luminance features are effective to characterize the quality degradation of pictorial regions of degraded SCIs caused by different distortion types.