# A Combined Full-Reference Image Quality Assessment Method Based on Convolutional Activation Maps
## 3 Experimental Results
### Comparison to the State-of-the-Art
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**MDID [48]**} & \multicolumn{4}{c}{**CSIQ [49]**} \\ \cline{2-9} \cline{5-9}  & **PLCC** & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline SSIM [12] & 0.581 & 0.576 & 0.411 & 0.812 & 0.812 & 0.606 \\ MS-SSIM [14] & 0.836 & 0.841 & 0.654 & 0.913 & 0.917 & 0.743 \\ MAD [49] & 0.742 & 0.725 & 0.533 & 0.950 & 0.947 & 0.796 \\ GSM [56] & 0.825 & 0.827 & 0.636 & 0.906 & 0.910 & 0.729 \\ HaarPSI [20] & 0.904 & 0.903 & 0.734 & 0.946 & 0.960 & 0.823 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison on MDID [48] and CSIQ [49] 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 ‘-’ if the data were not available. \begin{table}
\begin{tabular}{c|c|c|c|c|c|c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**VCL-FER [47]**} & \multicolumn{4}{c}{**TID2008 [46]**} \\ \cline{2-7}  & **PLCC** & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline SSIM [12] & 0.751 & 0.859 & 0.666 & 0.669 & 0.675 & 0.485 \\ MS-SSIM [14] & 0.917 & 0.925 & 0.753 & 0.838 & 0.846 & 0.648 \\ MAD [49] & 0.904 & 0.906 & 0.721 & 0.831 & 0.829 & 0.639 \\ GSM [56] & 0.904 & 0.905 & 0.721 & 0.782 & 0.781 & 0.578 \\ HaarPSI [20] & 0.938 & 0.946 & 0.789 & 0.916 & 0.897 & 0.723 \\ MDSI [57] & 0.935 & 0.939 & 0.774 & 0.877 & 0.892 & 0.724 \\ CSV [58] & 0.951 & 0.952 & 0.798 & 0.852 & 0.851 & 0.659 \\ GMSD [19] & 0.918 & 0.918 & 0.741 & 0.879 & 0.879 & 0.696 \\ DSS [59] & 0.925 & 0.927 & 0.757 & 0.860 & 0.860 & 0.672 \\ VSI [60] & 0.929 & 0.932 & 0.763 & 0.898 & 0.896 & 0.709 \\ PerSIM [61] & 0.926 & 0.928 & 0.761 & 0.826 & 0.830 & 0.655 \\ BLESS-SR-SIM [62] & 0.899 & 0.909 & 0.727 & 0.846 & 0.850 & 0.672 \\ BLESS-FSIM [62] & 0.927 & 0.924 & 0.751 & 0.853 & 0.851 & 0.669 \\ BLESS-FSIMc [62] & 0.932 & 0.935 & 0.768 & 0.871 & 0.871 & 0.687 \\ LCSIM1 [40] & - & - & - & 0.896 & 0.906 & 0.727 \\ ReSIFT [63] & 0.914 & 0.917 & 0.733 & 0.627 & 0.632 & 0.484 \\ IQ(\(\mathcal{L}_{T}\)) [28] & 0.910 & 0.912 & 0.718 & 0.841 & 0.840 & 0.629 \\ MS-UNIQUE [64] & 0.954 & 0.956 & 0.840 & 0.846 & 0.869 & 0.681 \\ SSIM CNN [41] & 0.917 & 0.921 & 0.743 & 0.770 & 0.737 & 0.551 \\ RVSIM [65] & 0.894 & 0.901 & 0.719 & 0.789 & 0.743 & 0.566 \\
2 stepQA [66] & 0.883 & 0.887 & 0.698 & 0.757 & 0.769 & 0.574 \\ SUMMER [67] & 0.750 & 0.754 & 0.596 & 0.817 & 0.823 & 0.637 \\ CEQI [68] & 0.894 & 0.920 & 0.747 & 0.887 & 0.891 & 0.714 \\ CEQIC [68] & 0.906 & 0.918 & 0.744 & 0.892 & 0.895 & 0.719 \\ VCGS [69] & 0.940 & 0.937 & 0.773 & 0.878 & 0.887 & 0.705 \\ DISTS [70] & 0.923 & 0.922 & 0.746 & 0.705 & 0.668 & 0.488 \\ \hline DeepFL-IQA [42] & - & - & - & - & - & - \\ BLINDER [43] & - & - & - & - & - & - \\ RankIQA [71] & - & - & - & - & - & - \\ BPSOM-MD [72] & - & - & - & - & - & - \\ NSSADNN [73] & - & - & - & - & - & - \\ \hline _ActMapFeat_ (ours) & **0.960** & **0.961** & **0.826** & **0.941** & **0.937** & **0.790** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison on VCL-FER [47] and TID2008 [46] 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 ‘-’ if the data were not available.