# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Performance on Individual Database  
The experiments on individual databases utilize four synthetically distorted databases, including LIVE, CSIQ, TID2013, and KADID, as well as two authentically distorted databases, LIVEC and KonIQ. The results are summarized in Tables 3 and 4. The methods compared with MT-IRN include three traditional methods (PSNR, SSIM [2], and BRISQUE [13]), seven deep learning-based methods (IQA-CNN [20], BIECON [53], MEON [54], DIQaM-NR [21], HyperIQA [22], DB-CNN [23], and TS-CNN [55]), two GAN-based methods (RAN4 IQA [16] and Hall-IQA [17]), and a visual compensation restoration-based method, namely VCRNet [33]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database** & **Ref. Imgs** & **Dist. Imgs** & **Dist. Types** & **Score’s Type** \\ \hline LIVE [39] & 29 & 779 & 5 & DMOS \\ CSIQ [40] & 30 & 866 & 6 & DMOS \\ TID2013 [41] & 25 & 3000 & 24 & MOS \\ KADID-10k [42] & 81 & 10,125 & 25 & DMOS \\ LIVEC [43] & / & 1162 & / & MOS \\ KonIQ-10k [44] & / & 10,073 & / & MOS \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details of the IQA databases. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LIVE**} & \multicolumn{2}{c}{**CSIQ**} & \multicolumn{2}{c}{**TID2013**} & \multicolumn{2}{c}{**KADID**} \\ \cline{2-9}  & **SROCC** & **PLCC** & **SROCC** & **PLCC** & **SROCC** & **PLCC** & **SROCC** & **PLCC** \\ \hline PSNR & 0.866 & 0.856 & 0.806 & 0.800 & 0.636 & 0.706 & 0.674 & 0.681 \\ SSIM [2] & 0.913 & 0.931 & 0.876 & 0.861 & 0.637 & 0.691 & 0.783 & 0.780 \\ BRISQUE [13] & 0.940 & 0.942 & 0.746 & 0.829 & 0.604 & 0.694 & 0.519 & 0.554 \\ IQA-CNN [20] & 0.956 & 0.953 & 0.876 & 0.905 & 0.701 & 0.752 & 0.651 & 0.607 \\ BIECON [53] & 0.961 & 0.962 & 0.825 & 0.838 & 0.717 & 0.762 & 0.685 & 0.691 \\ MEON [54] & 0.943 & 0.954 & 0.839 & 0.850 & 0.828 & 0.811 & 0.813 & 0.822 \\ DIQaM-NR [21] & 0.960 & 0.972 & 0.901 & 0.908 & 0.835 & 0.855 & 0.840 & 0.843 \\ HyperIQA [22] & 0.962 & 0.966 & 0.923 & 0.942 & 0.840 & 0.858 & **0.852** & **0.845** \\ DB-CNN [23] & 0.968 & 0.971 & **0.946** & **0.959** & 0.816 & 0.865 & 0.801 & 0.806 \\ TS-CNN [55] & 0.969 & **0.978** & 0.892 & 0.905 & 0.779 & 0.784 & 0.745 & 0.744 \\ RAN4 IQA [16] & 0.961 & 0.962 & 0.914 & 0.938 & 0.820 & 0.859 & / & / \\ Hall-IQA [17] & **0.976** & **0.978** & 0.892 & 0.906 & **0.879** & **0.880** & / & / \\ VCRNet [33] & **0.973** & 0.974 & **0.943** & **0.955** & 0.846 & 0.846 & 0.850 & 0.857 \\ MT-IRN & 0.969 & 0.970 & 0.928 & 0.943 & **0.852** & **0.877** & **0.877** & **0.878** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The SROCC and PLCC results on synthetically distorted databases. The top two results are shown in bold font. From the experimental results in Tables 3 and 4, it can be observed that MT-IRN outperforms the traditional methods on all six databases. This is mainly due to the powerful learning ability of deep learning, which enables the model to extract richer features. Compared with deep learning-based methods, our method performs better than most methods on the synthetically distorted databases, except DB-CNN on the CSIQ dataset. On the authentically distorted databases, our method performs slightly lower than Hyper-IQA, but it still achieves a better performance than the GAN-based RAN4 IQA. Particularly, on the LIVEC, our method's SROCC is approximately 45.9% higher than that of RAN4 IQA. This is mainly because RAN4 IQA is pre-trained on the synthetically distorted databases, while our method's score prediction sub-network uses ResNet-50, which is pre-trained on ImageNet and has a stronger ability to extract features from authentic distortions. Compared with the visual compensation restoration-based VCRNet, our method lags behind LIVE and CSIQ, but it still maintains a leading performance on the other four databases. This is because our method not only uses quality restoration features but also utilizes the difference features between the distorted and pseudo-reference images.