# Deep Activation Pooling for Blind Image Quality Assessment
## 3 Experiments
### Performance Comparison with Other Methods  
In Table 3, we compare the proposed method with three opinion-aware and two opinion-unaware methods on the CSIQ and LIVE databases. Since the opinion-aware methods learn regression models from distorted images, we partition each database into two subsets as in [19]. Specifically, distorted images correlated with 80% of the source images are used for training and the others are used for test. For fair comparison, we report the performance of the opinion-unaware methods on the partitioned test subset although they do not need training. From Table 3, we can draw several conclusions. First, the proposed HPSC+DAP is superior to all compared methods including opinion-aware methods  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & 3 & 4 & 5 & 6 & 7 & 8 \\ \hline
3 & 0.742 & 0.765 & 0.752 & 0.742 & 0.738 & 0.720 \\ \hline
4 & 0.769 & 0.787 & 0.784 & 0.771 & 0.767 & 0.753 \\ \hline
5 & 0.793 & 0.807 & 0.803 & 0.795 & 0.781 & 0.750 \\ \hline
6 & 0.804 & 0.815 & 0.811 & 0.802 & 0.794 & 0.780 \\ \hline
7 & 0.812 & **0.828** & 0.820 & 0.813 & 0.801 & 0.797 \\ \hline
8 & 0.802 & 0.814 & 0.808 & 0.794 & 0.783 & 0.769 \\ \hline \end{tabular}
\end{table}
Table 2: The SRCC of the CSM in different convolutional layers on the CSIQ database. The best result is highlighted in bold.  
Figure 6: The SRCC of the proposed HPSC+DAP under different \(K\) on the CSIQ database.  
and opinion-unaware methods. Second, compared with CORNIA, BRISQUE and BLIINDS2, which learn the regression model by using distortion images, the proposed HPSC+DAP achieves better performance without training on the LIVE database. Third, the proposed HPSC+DAP outperforms the SRCC and PRCC of the NIQE [17] by more than (0.2, 0.1) and (0.01, 0.01) on the CSIQ and LIVE databases, respectively. This is because the proposed HPSC+DAP utilizes the local TMVG model to capture local detail characteristics of distortion, while the NIQE only utilizes one global MVG model to describe an image. Fourth, the HPSP+DAP achieves better performance than IL-NIQE. The HPSP+DAP utilizes the DAP strategy to automatically emphasize the more important scores and suppress the less important ones, while the IL-NIQE method directly utilizes the average pooling to obtain the overall quality score. Fifth, the proposed HPSC+DAP obtains higher results than HPSP+DAP because the proposed HPSC+DAP utilizes the CSM in the convolutional layer, which could capture more meaningful information and remove the less important information, while the HPSP+DAP selects high-contrast patches on original images.  
Figure 7 shows seven images from the CSIQ database [24]. Figure 7a is a reference image, while the other six are distorted images of it. The difference MOS of Figure 7b-g are 0.325, 0.785, 0.352, 0.265, 0.586 and 0.805, respectively. A lower difference MOS indicates better perceptual quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**BIQA Models**} & \multicolumn{2}{c}{**CSIQ**} & \multicolumn{2}{c}{**LIVE**} \\ \cline{2-5}  & **SRCC** & **PRCC** & **SRCC** & **PRCC** \\ \hline CORNIA [27] & 0.714 & 0.781 & 0.940 & 0.944 \\ \hline BRISQUE [14] & 0.775 & 0.817 & 0.933 & 0.931 \\ \hline BLIINDS2 [26] & 0.780 & 0.832 & 0.924 & 0.927 \\ \hline NIQE [17] & 0.627 & 0.725 & 0.908 & 0.908 \\ \hline IL-NIQE [19] & 0.822 & 0.865 & 0.902 & 0.906 \\ \hline HPSP+DAP & 0.824 & 0.867 & 0.908 & 0.907 \\ \hline Proposed HPSC+DAP & **0.829** & **0.871** & **0.919** & **0.921** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation results of different methods on the CSIQ and the LIVE databases. The best results are highlighted in bold.