## 4 Experimental Results
### Individual Evaluation
#### 4.2.4 Comparison with the State-Of-The-Art  
The results of our method were compared to the state-of-the-art IQMs (PSNR, PSNR-HVS-M [89], PSNR-HA [89], C-PSNR-HVS-M [89], C-PSNR-HA [89], SSIM [18], CSSIM [90], CSSIM4 [90], WASH [91], VIF/VIF [92], IFC [93], UQI [94], WSN [95], SNR, NQM [96], MSSIM [97], FSIM [98], GMSD [99], CNN Quality [51]). In addition, we compared against no-reference IQMs (BRISQUE [100], DIVINE [101], AQI [102], ARISMC [103], BQMS [104], CPBD [105]). For fair comparison, the BRISQUE and DIVINE IQMs were retrained on both databases. Three distance-based metrics were also considered. The well-known metric Visual Difference Predictor (VDP) [106], that exploits the contrast sensitivity function that integrates the viewing distance, was evaluated against the proposed method. In [53], the authors improved the well-known metrics PSNR and SSIM by integrating an optimal scale selection model in discrete wavelet transform domain. This model aims to consider viewing distance and image resolution before using existing metrics. The latter are respectively labelled here as PSNR2 and SSIM2. Table 8 shows the results for both databases, the same results for Pearson correlation is shown in Figures 9-12. 95% confidence intervals were calculated using [PERSON]'s Z-transform. Globally, the correlations achieved on VDID2014 dataset were higher than those of CID:IQ. The metrics better predict the quality for 50 cm on CID:IQ, while higher correlations were obtained for 6*H on VDID2014. For FR approaches, the best performance on CID:IQ was obtained by CSSIM for 2.5*H and MSSIM for 5*H. CSSIM is a metric based on predictability of blocks simulating the visual system, and has also been shown to perform better than SSIM [90]. The main difference between SSIM and MSSIM is the multi scale analysis that allowed an improvement of 6% on CID:IQ. On VDID2014, CNN Quality achieved the best results for the two viewing distances. No-reference IQMs failed to predict quality for both databases, even after being retrained. Our distance-based method performed better than all the compared ones by more than 1.4% on CID:IQ. On VDID2014, our method obtained competitive results, since PSNR2 and SSIM2 performed better than our method for 6*H. However, our method is blind and thus does not need any information from the reference image. Furthermore, our method performed better than most of the FR metrics. To show the global performance of the suggested method, we calculated the correlation whatever the viewing distance and the degradation type. Tables 9 and 10 present the results on CID:IQ and VDID2014 datasets, respectively. Our method performed better than all the compared ones by more than 2.7% on CID:IQ. On VDID2014, the results show that the suggested IQM achieved the second best PCC value. However, our method remains highly competitive since most of the compared methods obtained a PCC smaller than 0.9 and best results (i.e., SSIM2 and PSNR2) were achieved by two FR metrics. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **All Patches** &
\begin{tabular}{c} **Saliency-Based** \\ **Patch Selection** \\ \end{tabular} \\ \hline
**CID:IQ** & 625 (\(\cong\)75 ms) & 180 (\(\cong\)21.6 ms) \\ \hline
**VDID** & 310 (\(\cong\)37.2 ms) & 180 (\(\cong\)26.6 ms) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean number of patches extracted per image on both datasets and their computation time. Figure 11: Performance of the IQMs on the VDID dataset for 4H viewing distance. The figure shows Pearson correlation with a 95% confidence interval. Figure 12: Performance of the IQMs on the VDID dataset for 6H viewing distance. The figure shows Pearson correlation with a 95% confidence interval. Figure 10: Performance of the IQMs on the CIDIQ dataset for a 100-cm viewing distance. The figure shows Pearson correlation with a 95% confidence interval. Figure 9: Performance of the IQMs on the CIDIQ dataset for 50 cm viewing distance. The figure shows Pearson correlation with a 95% confidence interval.