# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 3 Experimental Results and Analysis
### Comparison to the State of the Art  
To compare the proposed method to the state of the art, several NR-IQA methods have been collected, such as BLIINDER [66], DeepRN [67], BLIINDS-II [14], BMPRI [68], BRISQUE [15], CurveletQA [11], DIIVINE [9], ENIQA [69], GRAD-LOG-CP [70], MultiGAP-NRIQA [36], MSDF-IQA [40], NBIQA [71], PIQE [72], OG-IQA [73], SSEQ [74] and UNIQUE [75], whose original source codes are publicly available online. Excluding PIQE [72], which is an opinion-unaware method without a training step, these methods were evaluated using the same principle as the proposed method's on the applied IQA benchmark databases \begin{table}
\begin{tabular}{c c} \hline \hline
**Architecture** & **SROCC** \\ \hline VGG16 & 0.861 \\ ResNet50 & 0.860 \\ InceptionV3 & 0.909 \\ InceptionResNetV2 & 0.918 \\ DenseNet201 & 0.914 \\ NASNetMobile & 0.899 \\ \hline Average pooling of subscores & 0.927 \\ Median pooling of subscores & 0.931 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Base CNN and decision fusion comparison on KonIQ-10k [4]. Median SROCC values were measured over 100 random train–validation–test splits. (CLIVE [50], KonIQ-10k [4], SPAQ [5] and TID2013 [51]). As already mentioned, the median PLCC, SROCC and KROCC values are reported, which were measured over 100 random train-validation-test splits. Specifically, approximately 80% of images were in the training set, while the remaining 20% were in the test set, if a validation set is not required for an NR-IQA method. If a validation set is needed, then 70% of the images were in the training set, 10% in the validation set and 20% in the test set. Since TID2013 [51] contains images with artificial distortions, it was divided with respect to the reference images. Moreover, the performance results of several state-of-the-art deep learning-based methods, such as CONTRIQUE [76], DB-CNN [77], DeepFL-IQA [78], KonCept512 [79], MLSP [78; 80], PaQ-2-PiQ [46], PQR [81] and WSP [43], were copied from the authors' corresponding papers and added to our comparison. It is important to note that our proposed method was trained on down-sampled versions of KonIQ-10k [4] (512 \(\times\) 384 resolution instead of 1024 \(\times\) 768) and SPAQ [5] (2000 \(\times\) 2000 resolution instead of \(\sim\)4000 \(\times\) 4000) due to graphics processing unit (GPU) memory limitations and to accelerate the training process. Further, our proposed method is codenamed as _DF-CNN-IQA_ throughout the comparative tables published in this paper. The experimental results obtained on CLIVE [50] and KonIQ-10k [4] are summarized in Table 4. From these results, it can be observed that the proposed method outperforms all the other considered methods on KonIQ-10k [4] by a large margin. Specifically, it outperforms the second- and third-best methods by approximately 0.01 in terms of PLCC and SROCC. On the smaller CLIVE, the proposed method gives the second-best results among the methods which were tested by us. According to our experiments, the UNIQUE [75] method was able to exceed DF-CNN-IQA. The experimental results obtained on SPAQ [5] and TID2013 [51] are summarized in Table 5. From these results, it can be observed that DF-CNN-IQA exc