# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 4 Experiments
### Performance on Individual Distortion Types  
To compare the performance of MT-IRN with the state-of-the-art methods on specific types of distortions, experiments are conducted on LIVE, CSIQ and TID2013. The SROCC results of the experiments are summarized in Table 5.  
From Table 5, it can be observed that MT-IRN performs well in the experiments conducted on individual distortion types of the LIVE. MT-IRN utilizes a multitask image restoration subnetwork, which generates higher-quality pseudo-reference images. It achieves the best performance among the VCRNet on three distortion types, namely JP2K, JPEG, and FF, and obtains the second-best performance on the GB distortion. Although MT-IRN does not achieve the top two results on the WN distortion, it still outperforms the RAN4 IQA algorithm and other deep learning-based algorithms. Overall, MT-IRN demonstrates outstanding performance on all five distortion types in the LIVE and exhibits consistent performance across various distortion types without any apparent weaknesses. Particularly, it excels in handling JP2K, JPEG, and FF distortions, surpassing all other methods. These results indicate that MT-IRN is not only effective in handling various distortion types but also possesses robustness and stability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LIVEC**} & \multicolumn{2}{c}{**KonIQ**} \\ \cline{2-5}  & **SROCC** & **PLCC** & **SROCC** & **PLCC** \\ \hline BRISQUE [13] & 0.607 & 0.585 & 0.673 & 0.692 \\ IQA-CNN [20] & 0.516 & 0.536 & 0.655 & 0.671 \\ BIECON [53] & 0.595 & 0.613 & 0.618 & 0.651 \\ MEON [54] & 0.693 & 0.688 & 0.754 & 0.760 \\ DIQaM-NR [21] & 0.606 & 0.601 & 0.722 & 0.736 \\ HyperIQA [22] & **0.859** & **0.882** & **0.906** & **0.917** \\ DB-CNN [23] & 0.851 & 0.869 & 0.875 & 0.884 \\ TS-CNN [55] & 0.655 & 0.667 & 0.722 & 0.729 \\ RAN4 IQA [16] & 0.586 & 0.612 & 0.752 & 0.763 \\ VCRNet [33] & 0.856 & 0.865 & 0.894 & 0.909 \\ MT-IRN & **0.865** & **0.872** & **0.899** & **0.912** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The SROCC and PLCC results on authentically distorted databases. The top two results are shown in bold font. Regarding the CSIQ, MT-IRN achieves the best performance on JP2K, JPEG, and PN. Most methods struggle to achieve an SROCC of 0.900 on the CC distortion, whereas MT-IRN achieves an SROCC of 0.906, second only to VCRNet. Considering the individual distortion types on the CSIQ dataset, MT-IRN achieves the top two results on five out of the six distortion types, with three of them attaining the best performance, surpassing other methods. MT-IRN consistently achieves the top two performances in addressing the PN and CC distortion types, which are known to be challenging and not included in the Waterloo Exploration dataset. For the TID2013 dataset, MT-IRN achieves the top two performances in 13 out of the 24 distortion types. Remarkably, even in complex distortion types such as NPN, BW, MS, CC, and CCS, where most methods struggle to produce satisfactory results, MT-IRN consistently delivers excellent outcomes. Specifically, MT-IRN achieves SROCCs of 0.596, 0.728, 0.542, 0.786, and 0.719 on these distortion types. In contrast, GAN-based RAN4 IQA and Hall-IQA exhibit poor performance on these distortion types, failing to achieve an SROCC of 0.500. This is primarily attributed to the challenges faced by GAN-based methods in generating high-quality pseudo-reference images for severely distorted images, thereby affecting model performance. In contrast, MT-IRN not only extracts features from the pseudo-reference images but also leverages the image restoration features obtained during the restoration process. This enables the model to extract more useful prior information