# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 3 Experimental Results
### Performance Comparison for Different Distortion Categories  
To completely evaluate the predictive capability of the proposed BSRSF method for the distorted SCIs induced by different distortion types, we conduct comparison experiments of the BSRSF method and other methods on seven distortion types of the SIQAD database. The experimental results, namely the PLCC scores, are listed in Table 3. For each distortion type, three optimal PLCC scores in this table are indicated in boldface. On the basis of the experimental results in this table, we can draw two conclusions. Firstly, compared with other methods, the proposed BSRSF method obtains preferable experimental results for the majority of distortion types. To be more specific, the BSRSF method can derive accurate assessment results for six distortion types: GN, GB, MB, JPEG, JP2K and LSC. The reason for this is that the blur and compression can change the local structures of SCIs and the features used by the BSRSF method can precisely denote the degradation degree of local structures. Secondly, for the distortion type CC, the BSRSF method obtains a comparable PLCC score compared to the other top-three methods. In short, for different distortion types, the proposed BSRSF metric achieves better or clearly competitive predictive capability compared to other metrics, which further validates the robustness of the BSRSF metric.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Metrics**} & \multicolumn{1}{c}{**GN**} & \multicolumn{1}{c}{**GB**} & \multicolumn{1}{c}{**MB**} & \multicolumn{1}{c}{**CC**} & \multicolumn{1}{c}{**JPEG**} & \multicolumn{1}{c}{**JP2K**} & \multicolumn{1}{c}{**LSC**} \\ \hline \multirow{8}{*}{FR Metrics} & PSNR & **0.9053** & 0.8603 & 0.7044 & 0.7401 & 0.7545 & 0.7893 & 0.7805 \\  & SSIM & 0.8806 & 0.9014 & 0.8060 & 0.7435 & 0.7487 & 0.7749 & 0.7307 \\  & GMSD & 0.8956 & 0.9094 & 0.8436 & 0.7827 & 0.7746 & **0.8509** & **0.8559** \\  & SPQA & 0.8921 & 0.9058 & 0.8315 & **0.7992** & 0.7696 & 0.8252 & 0.7958 \\  & ESIM & 0.8891 & **0.9234** & **0.8886** & 0.7641 & **0.7999** & 0.7888 & 0.7915 \\  & SQI & 0.8829 & 0.9202 & 0.8789 & 0.7724 & **0.8218** & **0.8271** & **0.8310** \\  & SFUW & 0.8870 & 0.9230 & 0.8780 & **0.8290** & 0.7570 & 0.8150 & 0.7590 \\ \hline RR Metrics & RRSCI & 0.8798 & 0.8810 & 0.8465 & 0.6812 & 0.7638 & 0.6807 & 0.7110 \\ \hline \multirow{8}{*}{Blind Metrics} & BRISQUE & 0.8423 & 0.8247 & 0.7783 & 0.5548 & 0.7018 & 0.6823 & 0.5615 \\  & GWH-GLBP & 0.8537 & 0.8917 & 0.8297 & 0.4973 & 0.5687 & 0.7043 & 0.5678 \\  & IL-NIQE & 0.7667 & 0.5304 & 0.4136 & 0.1171 & 0.2945 & 0.4172 & 0.1754 \\ \cline{1-1}  & BQMS & 0.8353 & 0.8048 & 0.6969 & 0.5125 & 0.6686 & 0.7059 & 0.6562 \\ \cline{1-1}  & SIQE & 0.8590 & 0.8531 & 0.7817 & 0.5905 & 0.7639 & 0.7637 & 0.7752 \\ \cline{1-1}  & NRLT & **0.9101** & 0.8903 & **0.8865** & **0.7994** & 0.7851 & 0.7035 & 0.7219 \\ \cline{1-1}  & BLIQUP-SCI & 0.9015 & **0.9453** & 0.6341 & 0.7278 & 0.6691 & 0.6001 & 0.4253 \\ \cline{1-1}  & BSRSF & **0.9307** & **0.9405** & **0.9364** & 0.7807 & **0.8547** & **0.8554** & **0.8701** \\ \hline \hline \end{tabular}
\end{table}
Table 3: PLCC scores of metrics for seven degradation types in SIQAD.