# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 3 Experimental Results
### Performance Comparison Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Databases** & **Criteria** & **PSNR** & **SSIM** & **GMSD** & **SPQA** & **ESIM** & **SQI** & **SFUW** & **BSRSF** \\ \hline \multirow{3}{*}{SIQAD} & PLCC & 0.5869 & 0.7561 & 0.7259 & 0.8584 & 0.8788 & 0.8644 & **0.8910** & **0.8905** \\  & SROCC & 0.5605 & 0.7566 & 0.7305 & 0.8416 & 0.8632 & 0.8548 & **0.8800** & **0.8714** \\  & RMSE & 11.5876 & 9.3676 & 9.4684 & 7.3421 & **6.8310** & 7.1782 & **6.4990** & 7.2569 \\ \hline \multirow{3}{*}{SCID} & PLCC & 0.7622 & 0.7343 & 0.8337 & - & **0.8630** & - & **0.8590** & 0.7024 \\  & SROCC & 0.7512 & 0.7146 & 0.8138 & - & **0.8478** & - & **0.8950** & 0.7204 \\ \cline{1-1}  & RMSE & 9.1682 & 9.6133 & 7.8210 & - & **7.1552** & - & **7.3100** & 9.8849 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of the proposed BSRSF model and full-reference (FR) models. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Databases** & **Criteria** & **RRSCI** & **BRISQUE** & **GWH-GLBP** & **IL-NIQE** & **BQMS** & **SIQE** & **NRLT** & **BLIQUP-SCI** & **BSRSF** \\ \hline \multirow{3}{*}{SIQAD} & PLCC & 0.8014 & 0.7684 & 0.7903 & 0.3996 & 0.8108 & 0.7905 & **0.8387** & 0.7705 & **0.8905** \\  & SROCC & 0.7655 & 0.7094 & 0.7233 & 0.3496 & 0.7619 & 0.7609 & **0.8197** & 0.7909 & **0.8714** \\  & RMSE & 8.5620 & 8.2565 & 8.7480 & 13.2082 & 9.3110 & 8.7775 & **7.5847** & 10.0200 & **7.2569** \\ \hline \multirow{3}{*}{SCID} & PLCC & **0.6602** & 0.6137 & 0.6468 & 0.2569 & 0.6338 & 0.6457 & 0.6324 & - & **0.7024** \\  & SROCC & **0.7526** & 0.5795 & 0.6348 & 0.2432 & 0.6132 & 0.6022 & 0.6387 & - & **0.7204** \\ \cline{1-1}  & RMSE & 11.5401 & 12.2565 & 12.2831 & 13.6863 & 10.9519 & 10.9434 & **10.6327** & - & **9.8849** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of the proposed BSRSF model and reduced-reference (RR) and blind models. characteristics of textual and pictorial content in SCIs. Finally, among these FR methods, ESIM and SFUW are the top two prediction methods. From Table 2, it is clear that the designed NR BSRSF model achieves the maximal PLCC and SROCC scores and the minimum RMSE score on the two SCI databases. For SIQAD, the BSRSF method achieves, respectively, improvements of 6.2% and 6.3% against the other top blind method (NRLT) for PLCC and SROCC; meanwhile, it achieves an improvement of 4.3% against the other top blind method (NRLT) for RMSE. For SCID, the BSRSF method also derives similar experimental results. These results indicate that the proposed BSRSF method attains the best predictive ability among the compared blind and RR methods. Furthermore, the natural image evaluation methods which include BRISQUE, GWH-GLBP and IL-NIQE are weak in terms of evaluating the quality of a distorted SCI, because these methods do not carefully consider the features of the textual content in SCIs. In particular, IL-NIQUE delivers the worst predictive ability among all of the compared methods and the reason for this is that the NSS features employed in IL-NIQUE are unsuitable to represent the visual perception of distorted SCIs.