# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Performance on Individual Distortion Types
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Dist. Type** & **IQA-CNN [20]** & **DIQA [56]** & **HyperIQA** & **RAN4 IQA** & **Hall-IQA** & **VCRNet [33]** & **MT-IRN** \\ \hline \multirow{5}{*}{LIVE} & JP2K & 0.936 & 0.961 & 0.949 & 0.958 & 0.969 & **0.975** & **0.977** \\  & JPEG & 0.965 & 0.976 & 0.961 & 0.923 & 0.975 & **0.979** & **0.980** \\  & WN & 0.974 & 0.986 & 0.982 & 0.973 & **0.992** & **0.988** & 0.985 \\  & GB & 0.952 & 0.962 & 0.926 & 0.964 & **0.973** & 0.978 & **0.973** \\  & FF & 0.906 & 0.912 & 0.934 & 0.893 & 0.953 & **0.962** & **0.965** \\ \hline \multirow{5}{*}{CSIQ} & JP2K & 0.930 & 0.927 & 0.960 & 0.927 & 0.924 & **0.962** & **0.963** \\  & JPEG & 0.915 & 0.931 & 0.934 & 0.904 & 0.933 & **0.956** & **0.958** \\  & WN & 0.919 & 0.835 & 0.927 & 0.923 & **0.942** & **0.939** & 0.934 \\  & GB & 0.918 & 0.870 & 0.915 & 0.889 & 0.901 & **0.950** & **0.942** \\  & PN & 0.900 & 0.893 & **0.931** & 0.844 & 0.842 & 0.899 & **0.946** \\  & CC & 0.786 & 0.718 & 0.874 & 0.860 & 0.861 & **0.919** & **0.906** \\ \hline \multirow{5}{*}{TID2013} & AGN & 0.784 & 0.916 & **0.942** & 0.866 & **0.923** & 0.844 & 0.892 \\  & ANC & 0.758 & 0.755 & **0.916** & 0.753 & **0.880** & 0.785 & 0.768 \\  & SCN & 0.762 & 0.878 & **0.947** & 0.842 & 0.945 & 0.787 & **0.961** \\  & MN & 0.776 & 0.734 & **0.801** & 0.462 & 0.673 & **0.795** & 0.781 \\  & HFN & 0.816 & 0.939 & **0.955** & 0.908 & **0.955** & 0.942 & 0.894 \\  & IN & 0.807 & 0.844 & 0.855 & 0.855 & 0.810 & **0.876** & **0.892** \\  & QN & 0.616 & **0.858** & 0.726 & 0.849 & 0.831 & 0.847 & **0.875** \\  & GB & **0.921** & 0.920 & **0.969** & 0.833 & 0.832 & 0.906 & 0.899 \\  & DEN & 0.872 & 0.788 & **0.941** & 0.839 & **0.957** & 0.937 & 0.880 \\  & JPEG & 0.874 & 0.892 & 0.898 & **0.939** & 0.914 & **0.934** & 0.897 \\  & JP2K & 0.910 & 0.812 & **0.947** & 0.912 & 0.624 & 0.906 & **0.918** \\  & JGTE & 0.686 & 0.862 & **0.934** & 0.566 & 0.460 & 0.762 & **0.852** \\  & JETE & 0.678 & 0.813 & **0.892** & 0.778 & 0.782 & 0.865 & **0.892** \\  & NPN & 0.286 & 0.160 & **0.808** & 0.234 & **0.664** & 0.457 & 0.596 \\  & BW & 0.219 & 0.408 & 0.361 & 0.339 & 0.122 & **0.601** & **0.728** \\  & MS & **0.565** & 0.300 & 0.374 & 0.135 & 0.182 & 0.509 & **0.542** \\  & CC & 0.182 & 0.447 & **0.753** & 0.578 & 0.376 & 0.595 & **0.786** \\  & CCS & 0.081 & 0.151 & **0.857** & 0.484 & 0.156 & **0.855** & 0.719 \\  & MGN & 0.644 & **0.904** & 0.899 & 0.787 & 0.850 & 0.845 & **0.900** \\  & CN & 0.534 & 0.656 & **0.960** & 0.819 & 0.614 & 0.804 & **0.840** \\  & LCNI & 0.810 & 0.830 & **0.897** & 0.895 & 0.852 & 0.816 & **0.913** \\  & ICQD & 0.272 & **0.937** & 0.901 & 0.822 & 0.911 & **0.945** & 0.867 \\  & CHA & 0.892 & 0.757 & **0.870** & 0.762 & 0.381 & **0.932** & 0.828 \\  & SSR & 0.910 & 0.909 & 0.910 & 0.917 & 0.616 & **0.948** & **0.922** \\ \hline \multirow{2}{*}{Count} & 2 & 3 & 17 & 1 & 8 & **18** & **22** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The SROCC results of the individual distortion types on LIVE, CSIQ, and TID2013. The top two results are shown in bold font. “Count” refers to the number of times a method achieves the top two results. from both the image restoration features and the differential features when confronted with heavily distorted images. Additionally, the utilization of multiple scales of distortion features enhances the model's performance. Overall, MT-IRN achieves the top two performances on 22 out of 35 distortion types, outperforming other methods. This demonstrates good performance for specific distortion types, even when facing relatively complex distortions. This is mainly due to the multitask restoration network used in our method, which improves the quality of the generated pseudo-reference images through the mutual promotion of the two tasks. In addition, our method uses not only differential features but also quality restoration features, enabling the score prediction sub-network to make more comprehensive and accurate predictions of image quality scores.