# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 3 Experimental Results
### Performance Comparison Experiments  
In this study, the proposed BSRSF method is compared with the following existing IQA models: PSNR, SSIM [3], GMSD [6], SPQA [18], ESIM [21], SFUW [20], SQI [48], RRSCI [25], BRISQUE [11], GWH-GLBP [13], IL-NIQE [16], BQMS [28], SIQE [17], NRLT [33] and BLIQUP-SCI [29]. Among these models, PSNR, SSIM, GMSD, SPQA, ESIM, SFUW and SQI are FR IQA models, RRSCI is an RR IQA model and BRISQUE, GWH-GLBP, IL-NIQE, BQMS, SIQE, NRLT and BLIQUP-SCI are blind IQA models. Additionally, SPQA, ESIM, SFUW, SQI, RRSCI, BQMS, SIQE, NRLT and BLIQUP-SCI have been specifically devised to evaluate the quality of SCIs. For three FR metrics SPQA, SQI and SFUW, and one RR metric RRSCI, the experimental results are directly obtained from their references. For the rest of the FR metrics, the results are calculated by running the source codes provided by their authors. For the blind metrics, the results of BLIQUP-SCI are directly taken from its reference. For the rest of the blind metrics, their source codes are used to derive experimental results. For the proposed BSRSF metric and learning-based blind metrics including BRISQUE, GWH-GLBP, IL-NIQE, BQMS, SIQE and NRLT, an SCI database is randomly split into two subsets: the training subset and the evaluation subset. The training subset includes 80% SCIs of this database and the evaluation subset includes 20% SCIs of this database. The distorted SCIs in the training subset are used to train the model and then this trained model is used to evaluate the quality of distorted SCIs in the evaluation subset. This train-evaluate operation is repeated 1000 times on this database and the median experimental results across 1000 train-evaluate operations are reported. In the proposed metric, the LibSVM package [49], is employed as the SVR tool. When the \(\varepsilon\)-SVR is employed to learn the regression models, two parameters (\(C\), \(\rho\)) of the \(\varepsilon\)-SVR need to be decided. In our experiments, a grid search in the logarithm space is used to estimate the optimal values of \(C\) and \(\rho\)[47]. For the regression model of textual regions of SCIs, the optimal values of (\(C\), \(\rho\)) are found to be (16,384, 2) and (256, 16) on SIQAD and SCID, respectively. For the regression model of pictorial regions of SCIs, the optimal values of (\(C\), \(\rho\)) are found to be (8192, 4) and (512, 0.5) on SIQAD and SCID, respectively. Experimental results are tabulated in Tables 1 and 2, and the best two results of each row are highlighted in boldface. Furthermore, as the papers of SPQA, SQI and BLIQUP-SCI do not provide the experimental results for SCID, these results are absent in these two tables. From Table 1, we can draw three conclusions. Firstly, the proposed NR BSRSF method has a competitive predictive ability in comparison to the FR SCI evaluation methods which include SPQA, ESIM, SQI and SFUW; meanwhile, it achieves preferable performance in contrast with the traditional FR natural image evaluation methods which include PSNR, SSIM and GMSD. Secondly, for SCIs, the four dedicated SCI evaluation models which include SPQA, ESIM, SQI and SFUW achieve better performance than the traditional IQA models which include PSNR, SSIM and GMSD. The reason for this is that these dedicated models carefully deal with the distinctions between the visual characteristics of textual and pictorial regions in SCIs, while traditional IQA methods equally consider the visual