# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 3 Experimental Results and Analysis
### Comparison to the State of the Art
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Direct Average**} & \multicolumn{3}{c}{**Weighted Average**} \\ \hline
**Method** & **PLCC** & **SROCC** & **KROCC** & **PLCC** & **SROCC** & **KROCC** \\ \hline BLIINDER [66] & 0.841 & 0.828 & 0.662 & 0.865 & 0.856 & 0.676 \\ DeepRN [67] & 0.816 & 0.780 & 0.620 & 0.850 & 0.832 & 0.654 \\ BLIINDS-II [14] & 0.570 & 0.551 & 0.383 & 0.612 & 0.605 & 0.431 \\ BMPRI [68] & 0.655 & 0.607 & 0.422 & 0.685 & 0.660 & 0.455 \\ BRISQUE [15] & 0.609 & 0.580 & 0.409 & 0.680 & 0.658 & 0.472 \\ CurveletQA [11] & 0.678 & 0.655 & 0.445 & 0.732 & 0.713 & 0.479 \\ DIIVINE [9] & 0.698 & 0.657 & 0.455 & 0.731 & 0.704 & 0.482 \\ ENIQA [69] & 0.694 & 0.667 & 0.480 & 0.758 & 0.740 & 0.545 \\ GRAD-LOG-CP [70] & 0.692 & 0.677 & 0.481 & 0.732 & 0.721 & 0.523 \\ MultiGAP- & 0.844 & 0.765 & 0.588 & 0.885 & 0.846 & 0.659 \\ NRIQA [36] & 0.840 & 0.757 & 0.578 & 0.877 & 0.833 & 0.647 \\ MSDI-IQA [71] & 0.731 & 0.694 & 0.477 & 0.772 & 0.747 & 0.511 \\ PIQE [72] & 0.264 & 0.219 & 0.150 & 0.238 & 0.214 & 0.142 \\ OG-IQA [73] & 0.622 & 0.579 & 0.432 & 0.669 & 0.646 & 0.493 \\ SSEQ [74] & 0.610 & 0.568 & 0.414 & 0.656 & 0.634 & 0.467 \\ UNIQUE [57] & 0.878 & 0.871 & 0.641 & 0.892 & 0.891 & 0.662 \\ \hline CONTRIQUE [76] & **0.885** & **0.874** & - & 0.904 & **0.894** & - \\ DB-CNN [77] & 0.883 & 0.864 & - & 0.894 & 0.884 & - \\ DeepFL-IQA [78] & - & - & - & - & - & - \\ KorCept512 [79] & - & - & - & - & - & - \\ MLSP [78, 80] & - & - & - & - & - & - \\ PaQ-2-PO [46] & - & - & - & - & - & - \\ PQR [81] & - & - & - & - & - & - \\ WSP [43] & - & - & - & - & - & - \\ \hline DF-CNN-IQA & 0.868 & 0.851 & 0.639 & **0.908** & **0.894** & **0.685** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the proposed _DF-CNN-IQA_ to the state of the art. The weighted and direct average of measured PLCC and SROCC values are reported. The best results are typed in **bold**, and the second-best results are underlined. We denoted by ’-’ if the data were not available. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **CLIVE [50]** & **KonIQ-10k [4]** & **SPAQ [5]** & **TID2013 [51]** \\ \hline BLIINDER [66] & 1 & 1 & 1 & \(-1\) \\ DeepRN [67] & 1 & 1 & 1 & 1 \\ BLIINDS-II [14] & 1 & 1 & 1 & 1 \\ BMPRI [68] & 1 & 1 & 1 & 1 \\ BRISQUE [15] & 1 & 1 & 1 & 1 \\ CurveletQA [11] & 1 & 1 & 1 & 1 \\ DIIVINE [9] & 1 & 1 & 1 & 1 \\ ENIQA [69] & 1 & 1 & 1 & 1 \\ GRAD-LOG-CP [70] & 1 & 1 & 1 & 1 \\ MultiGAP-NRIQA [36] & 1 & 1 & 1 & 1 \\ MSDF-IQA [40] & 1 & 1 & 1 & 1 \\ NBIQA [71] & 1 & 1 & 1 & 1 \\ PGUE [72] & 1 & 1 & 1 & 1 \\ OG-IQA [73] & 1 & 1 & 1 & 1 \\ SSEQ [74] & 1 & 1 & 1 & 1 \\ UNIQUE [75] & \(-1\) & 1 & 1 & \(-1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of the one-sided \(t\)-tests. Symbol 1 (\(-1\)) means that the proposed _DF-CNN-IQA_ method is statistically significantly (95% confidence interval, \(p=0.05\) significance level) better (worse) than the NR-IQA method in the row on the IQA benchmark database in the column.