## 4 Experiments
### Performance Evaluation on Authentic Distortion  
An excellent quality metric should not only achieve good performance in synthetic distortion but also evaluate authentic distortion accurately. Therefore, the performance of the proposed HFNet was evaluated on the authentic distortion datasets, which is described in this subsection. For performance comparison, the same verification was conducted by 20 representative quality metrics. Specifically, it contained the same six handcrafted quality methods as in the previous subsection and 14 deep-learning-based quality metrics, including BIECON [34], PQR [35], WaDIaM-NR [24], RAN4 IQA [36], SFA [37], NSSADNN [38],  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Published**} & \multicolumn{2}{c}{**CSIQ**} & \multicolumn{2}{c}{**TID2013**} \\  & & **SRCC** & **PLCC** & **SRCC** & **PLCC** \\ \hline DIIVINE [61] & TIP11 & 0.777 & 0.743 & 0.535 & 0.664 \\ BRISQUE [22] & TIP12 & 0.746 & 0.829 & 0.604 & 0.694 \\ CORNIA [26] & CVPR12 & 0.678 & 0.776 & 0.678 & 0.768 \\ NIQE [27] & SPL13 & 0.821 & 0.865 & 0.521 & 0.648 \\ ILNIQE [21] & TIP15 & 0.806 & 0.808 & - & - \\ HOSA [28] & TIP16 & 0.741 & 0.823 & 0.735 & 0.815 \\ \hline BIECON [34] & JSTSP17 & 0.815 & 0.823 & 0.717 & 0.762 \\ PQR [35] & ICIP18 & 0.873 & 0.901 & - & - \\ WaDIaM-NR [24] & TIP18 & **0.955** & **0.973** & 0.761 & 0.787 \\ RAN4 IQA [36] & AAAI18 & 0.914 & 0.931 & 0.820 & 0.859 \\ SFA [37] & TMM19 & 0.796 & 0.818 & - & - \\ NSSADNN [38] & TMM19 & 0.893 & 0.927 & 0.844 & **0.910** \\ HyperNet [25] & CVPR20 & 0.923 & 0.942 & - & - \\ DBCNN [39] & TCSVT20 & 0.946 & 0.959 & 0.816 & 0.865 \\ CaHDC [40] & TIP20 & 0.903 & 0.914 & **0.862** & 0.878 \\ UNIQUE [62] & TIP21 & 0.902 & 0.927 & - & - \\ CLRIQA [63] & TMM22 & 0.915 & 0.938 & 0.837 & 0.863 \\ VCRNet [64] & TIP22 & 0.943 & 0.955 & 0.846 & 0.875 \\ HFNet & & **0.956** & **0.964** & **0.933** & **0.911** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of the proposed HFNet and the compared algorithms on the synthetic image databases.  
HyperNet [25], DBCNN [39], CaHDC [40], UNIQUE [62], CLRIQA [63], VCRNet [64], MetalQA+ [8], and IEIT [41]. The experimental results of the authentic image databases are shown in Table 3, where the top two results are shown in bold.  
It can be observed from the table that the proposed HFNet achieved promising results. For the BID dataset, the HFNet was superior to all the other compared algorithms, producing 0.883 and 0.897 for the SRCC and PLCC, respectively. Although HyperNet achieved second place, both indexes were significantly lower than the two indexes of the proposed HFNet. In contrast, the other algorithms achieved only modest results. For the LIVEC, the performance of the proposed HFNet was higher than that of the other algorithms. It achieved 0.901 for the SRCC and 0.908 for the PLCC, both of which were significant improvements over the previous algorithms. The MetaIQA+ and DBCNN achieved similar results. For KonIQ-10k, both NSSADNN and the proposed HFNet showed promising results. HyperNet and MetaIQA+ achieved similar results. In addition, the traditional methods had lower results than the deep-learning-based methods in terms of the average performance. As summarized in the section on the related work, they have difficulty dealing with complex authentic distortion. From the above analysis and results, it can be confirmed that the proposed HFNet achieved a SOTA performance, which depended on the proposed hierarchical feature fusion strategy and the mechanism of simulating the human visual system.