## 4 Experiments
### Ablation Studies  
In a quality metric, the selection of the backbone network and the design of the main modules are very important to the performance. So, we conducted an ablation studies experiment in two parts, the impact of the backbone on the performance and the local and global features on the performance. The first part tested the effect of different versions of backbone on the performance of the proposed HFNet. According to the existing literature reports, PVT-V2 is outstanding in computer vision tasks. However, it has several different versions, depending on the network configuration. To be specific, this network included PVTv2-b0, PVTv2-b1, PVTv2-b2, PVTv2-b3, PVTv2-b4, and PVTv2-b5 [50]. The performances (SRCC and PLCC) of \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Published**} & \multicolumn{2}{c}{**BID**} & \multicolumn{2}{c}{**LIVEC**} & \multicolumn{2}{c}{**KonIQ-10k**} \\  & & **SRCC** & **PLCC** & **SRCC** & **PLCC** & **SRCC** & **PLCC** \\ \hline DIIVINE [61] & TIP11 & - & - & 0.523 & 0.551 & 0.579 & 0.632 \\ BRISQUE [22] & TIP12 & 0.562 & 0.593 & 0.608 & 0.629 & 0.665 & 0.681 \\ CORNIA [26] & CVPR12 & - & - & 0.618 & 0.662 & 0.738 & 0.773 \\ NIQE [27] & SPL13 & - & - & 0.594 & 0.589 & - & - \\ ILNIQE [21] & TIP15 & 0.516 & 0.554 & 0.432 & 0.508 & 0.507 & 0.523 \\ HOSA [28] & TIP16 & 0.721 & 0.736 & 0.640 & 0.678 & 0.761 & 0.791 \\ \hline BIECON [34] & JSTSP17 & - & - & 0.595 & 0.613 & - & - \\ PQR [35] & ICIP18 & 0.775 & 0.794 & 0.857 & 0.882 & 0.880 & 0.884 \\ WaDiM-NR [24] & TIP18 & 0.725 & 0.742 & 0.671 & 0.680 & 0.797 & 0.805 \\ RAN4 IQA [36] & AAAI18 & - & - & 0.586 & 0.612 & 0.752 & 0.763 \\ SFA [37] & TMM19 & 0.826 & 0.840 & 0.812 & 0.833 & 0.856 & 0.872 \\ NSSADNN [38] & TMM19 & - & - & 0.745 & 0.813 & **0.912** & 0.887 \\ HyperNet [25] & CVPR20 & **0.869** & **0.878** & **0.859** & 0.882 & 0.906 & 0.917 \\ DBCNN [39] & TCSVT20 & 0.845 & 0.859 & 0.851 & 0.869 & 0.868 & 0.892 \\ CaHDC [40] & TIP20 & - & - & 0.738 & 0.744 & - & - \\ UNIQUE [62] & TIP21 & 0.858 & 0.873 & 0.854 & **0.890** & 0.896 & 0.901 \\ CLRIQA [63] & TMM22 & - & - & 0.832 & 0.866 & 0.831 & 0.846 \\ MetaIQA+ [8] & TCSVT22 & - & - & 0.852 & 0.872 & 0.909 & **0.921** \\ VCRNet [64] & TIP22 & - & - & 0.856 & 0.865 & 0.894 & 0.909 \\ IEIT [41] & TCSVT22 & - & - & 0.833 & 0.865 & 0.892 & 0.916 \\ HFNet & & **0.883** & **0.897** & **0.901** & **0.908** & **0.910** & **0.928** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experimental results of the proposed HFNet and the compared algorithms on the authentic image databases. the different backbones on the LIVEC database are shown in Figure 4. As seen in the figure, the performance values were highest when using the PVTv2-b5 as the backbone. In contrast, the PVTv2-b0 had the worst performance due to its inadequate configuration. Based on these results, the PVTv2-b5 was selected as the backbone and was applied in all the experiments. The second part of the experiment was to study the influence of the experimental results of the local and global features in the feature fusion module. When testing the contribution of the local features to the measurement, we extracted only the local features for fusion in the fusion module but not the global features, and the opposite was true when testing the global features. The experiments were carried out on five databases, namely, two synthetic distortion databases (CSIQ and TID2013) and three authentic distortion databases (BID, LIVEC, and KONIQ-10K). The experimental results of the synthetic distortion databases are shown in Table 4, and the experimental results of the authentic distortion databases are shown in Table 5, where the best performances are shown in bold. As can be seen from the table, both the local and global features achieved a moderate performance. The performance was further improved when the two parts were combined.