# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 3 Experimental Results
### Comparison to the State-of-the-Art
Figure 7 depicts scatter plots of ground-truth MOS values against predicted MOS values on MDID [48], TID2008 [46], TID2013 [53], VCL-FER [47], KADID-10k [44], and CSIQ [49] test sets. Figure 8 depicts the box plots of the measured PLCC, SROCC, and KROCC values over 100 random train-test splits. On each box, the central mark denotes the median, and the bottom and top edges of the box represent the 25 th and 75 th percentiles, respectively. Moreover, the whiskers extend to the most extreme values which are not considered outliers. \begin{table}
\begin{tabular}{c|c|c|c|c|c|c} \hline \hline  & \multicolumn{4}{c}{**KADID-10k [44]**} & \multicolumn{4}{c}{**TID2013 [53]**} \\ \cline{2-7}  & **PLCC** & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline SSIM [12] & 0.670 & 0.671 & 0.489 & 0.618 & 0.616 & 0.437 \\ MS-SSIM [14] & 0.819 & 0.821 & 0.630 & 0.794 & 0.785 & 0.604 \\ MAD [49] & 0.716 & 0.724 & 0.535 & 0.827 & 0.778 & 0.600 \\ GSM [56] & 0.780 & 0.780 & 0.588 & 0.789 & 0.787 & 0.593 \\ HaarPSI [20] & 0.871 & 0.885 & 0.699 & 0.886 & 0.859 & 0.678 \\ MDSI [57] & 0.887 & 0.885 & 0.702 & 0.867 & 0.859 & 0.677 \\ CSV [58] & 0.671 & 0.669 & 0.531 & 0.852 & 0.848 & 0.657 \\ GMSD [19] & 0.847 & 0.847 & 0.664 & 0.846 & 0.844 & 0.663 \\ DSS [59] & 0.855 & 0.860 & 0.674 & 0.793 & 0.781 & 0.604 \\ VSI [60] & 0.874 & 0.861 & 0.678 & 0.900 & 0.894 & 0.677 \\ PerSIM [61] & 0.819 & 0.824 & 0.634 & 0.825 & 0.826 & 0.655 \\ BLESS-SR-SIM [62] & 0.820 & 0.824 & 0.633 & 0.814 & 0.828 & 0.648 \\ BLESS-FSIM [62] & 0.814 & 0.816 & 0.624 & 0.824 & 0.830 & 0.649 \\ BLESS-FSIMc [62] & 0.845 & 0.848 & 0.658 & 0.846 & 0.849 & 0.667 \\ LCSIM [40] & - & - & - & 0.914 & 0.904 & 0.733 \\ ReSIFT [63] & 0.648 & 0.628 & 0.468 & 0.630 & 0.623 & 0.471 \\ IQ(\(\mathcal{L}_{T}\)) [28] & 0.853 & 0.852 & 0.641 & 0.844 & 0.842 & 0.631 \\ MS-UNIQUE [64] & 0.845 & 0.840 & 0.648 & 0.865 & 0.871 & 0.687 \\ SSIM CNN [41] & 0.811 & 0.814 & 0.630 & 0.759 & 0.752 & 0.566 \\ RVSIM [65] & 0.728 & 0.719 & 0.540 & 0.763 & 0.683 & 0.520 \\
2 stepQA [66] & 0.768 & 0.771 & 0.571 & 0.736 & 0.733 & 0.550 \\ SUMMER [67] & 0.719 & 0.723 & 0.540 & 0.623 & 0.622 & 0.472 \\ CEQI [68] & 0.862 & 0.863 & 0.681 & 0.855 & 0.802 & 0.635 \\ CEQIc [68] & 0.867 & 0.864 & 0.682 & 0.858 & 0.851 & 0.638 \\ VCGS [69] & 0.873 & 0.871 & 0.683 & 0.900 & 0.893 & 0.712 \\ DISTS [70] & 0.809 & 0.814 & 0.626 & 0.759 & 0.711 & 0.524 \\ \hline DeepFLI-IQA [42] & 0.938 & 0.936 & - & 0.876 & 0.858 & - \\ BLINDER [43] & - & - & - & 0.819 & 0.838 & - \\ RankIQA [71] & - & - & - & 0.799 & 0.780 & - \\ BPSOM-MD [72] & - & - & - & 0.879 & 0.863 & - \\ NSSADNN [73] & - & - & - & 0.910 & 0.844 & - \\ \hline _ActMapFeat_ (ours) & **0.959** & **0.957** & **0.819** & **0.943** & **0.936** & **0.780** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison on KADID-10k [44] and TID2013 [53] databases. Mean PLCC, SROCC, and KROCC values are reported for the learning-based approaches measured over 100 random train-test splits. The best results are typed in **bold**. The green background color stands for the fact that the correlation is lower than those of the proposed method and the difference is statistically significant with \(p<0.05\). We used ’\(\cdot\)’ if the data were not available. Figure 7: Scatter plots of the ground-truth MOS against the predicted MOS of _ActMapFeat_ on different test sets of MDID [48], TID2008 [46], TID2013 [53], VCL-FER [47], KADID-10k [44], and CSIQ [49] IQA benchmark databases. (**a**) MDID [48]. (**b**) TID2008 [46]. (**c**) TID2013 [53]. (**d**) VCL-FER [47]. (**e**) KADID-10k [44]. (**f**) CSIQ [49]. Figure 8: Box plots of the measured PLCC, SROCC, and KROCC values produced by the proposed _ActMapFeat_ over 100 random train-test splits on MDID [48], TID2008 [46], TID2013 [53], VCL-FER [47], KADID-10k [44], and CSIQ [49] IQA benchmark databases. (**a**) MDID [48]. (**b**) TID2008 [46]. (**c**) TID2013 [53]. (**d**) VCL-FER [47]. (**e**) KADID-10k [44]. (**f**) CSIQ [49].