# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Ablation Experiments  
To evaluate the impact of each module on the performance of MT-IRN, ablation experiments are conducted on the LIVE, CSIQ, and LIVEC databases, and the results are summarized in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Training** & \multicolumn{3}{c}{**LIVE**} & \multicolumn{3}{c}{**CSIQ**} \\ \hline
**Testing** & **CSIQ** & **TID2013** & **LIVEC** & **LIVE** & **TID2013** & **LIVEC** \\ \hline DIIVINE [57] & 0.582 & 0.373 & 0.300 & 0.815 & 0.419 & 0.366 \\ CORNIA [58] & 0.620 & 0.382 & 0.431 & 0.843 & 0.331 & 0.393 \\ HOSA [59] & 0.598 & 0.470 & 0.455 & 0.770 & 0.341 & 0.309 \\ DB-CNN [23] & 0.758 & **0.524** & 0.567 & 0.877 & 0.540 & 0.452 \\ RAN4 IQA [16] & 0.632 & 0.462 & 0.157 & 0.806 & 0.471 & 0.116 \\ Hall-IQA [17] & 0.668 & 0.486 & 0.126 & 0.833 & 0.491 & 0.107 \\ VCRNet [33] & **0.768** & 0.502 & **0.615** & **0.886** & **0.542** & **0.463** \\ MT-IRN & **0.783** & **0.565** & **0.600** & **0.892** & **0.573** & **0.467** \\ \hline
**Training** & \multicolumn{3}{c}{**TID2013**} & \multicolumn{3}{c}{**LIVEC**} \\ \hline
**Testing** & **LIVE** & **CSIQ** & **LIVEC** & **LIVE** & **CSIQ** & **TID2013** \\ \hline DIIVINE [57] & 0.714 & 0.585 & 0.230 & 0.362 & 0.417 & 0.337 \\ CORNIA [58] & 0.829 & 0.662 & 0.267 & 0.578 & 0.456 & 0.403 \\ HOSA [59] & 0.844 & 0.609 & 0.253 & 0.537 & 0.336 & 0.399 \\ DB-CNN [23] & **0.891** & **0.807** & **0.457** & **0.746** & **0.697** & **0.424** \\ RAN4 IQA [16] & 0.795 & 0.673 & 0.101 & 0.297 & 0.286 & 0.153 \\ Hall-IQA [17] & 0.786 & 0.683 & 0.116 & - & - & - \\ VCRNet [33] & 0.822 & 0.721 & 0.307 & **0.746** & **0.566** & 0.416 \\ MT-IRN & **0.897** & **0.739** & **0.375** & **0.758** & 0.546 & **0.419** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The SROCC results of the cross-database test. The top two results are shown in bold font. First, the score prediction sub-network with only distorted images as input is used as the baseline, and its performance is the worst, with SROCCs of 0.950, 0.894, and 0.820 on the three databases, respectively. Then, the single-task image quality restoration sub-network is added, and the image restoration features are directly concatenated with the multi-scale content features of the distorted images. At this point, the model is able to utilize some information from pseudo-reference images, resulting in an improvement in performance, with SROCC increasing by 0.008, 0.013, and 0.013, respectively. Next, the multitask image quality restoration sub-network is used, but only the image restoration features are used. The quality of the pseudo-reference images is improved, resulting in an improvement in model performance, with SROCC increasing by 0.003, 0.013, and 0.009, respectively. Then, the image difference feature is introduced, allowing the model to more fully utilize the information from the pseudo-reference images, resulting in further improvements in SROCCs of 0.004, 0.006, and 0.005, respectively. Finally, the multi-scale feature fusion module is introduced, allowing for the full fusion of image multi-scale content features and restoration features, and the model's performance reaches its best, with SROCCs improved by 0.004, 0.008, and 0.005, respectively. Figure 6 illustrates the performance of the model on three datasets as different modules are progressively integrated into the baseline. The figure demonstrates that as the multitask restoration network, difference feature, and multi-scale feature fusion module are successively incorporated into the model, its performance consistently improves. Notably, the inclusion of the multitask restoration network results in the most significant enhancement in the model's performance.