# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 4 Conclusions  
In this paper, we proposed a novel deep learning-based approach for NR-IQA. Specifically, a decision fusion of different deep convolutional architectures, such as VGG16 [56], ResNet50 [57], InceptionV3 [42], InceptionResNetV2 [59], DenseNet201 [60] and NAS-NetMobile [61], were carried out to for perceptual image quality estimation. The main motivation behind this layout was the following. The first layers of a CNN frequently capture basic image structures, i.e., edges, blobs or color patterns, which are quality-aware features. Using a diverse set of pretrained CNNs allowed us to better characterize natural images' authentic distortions. This motivation was confirmed in a parameter study where it was demonstrated that a decision fusion of CNNs is able to considerably improve the performance of estimation. In a series of comparative experiments, it has been shown that the proposed method is able to reach or outperform the state of the art on two IQA benchmark databases with authentic distortions. The obtained results were confirmed in significance and cross-database tests. Future work will focus on a feature-level fusion and joint training of CNNs to reduce the training time and to achieve simpler architectures. Another direction of research is to generalize the achieved results for specific applications, such as the quality assessment of computer tomography (CT) or magnetic resonance imaging (MRI) images. The source code of the proposed is available at: [[https://github.com/Skythianos/DF-CNN-IQA](https://github.com/Skythianos/DF-CNN-IQA)]([https://github.com/Skythianos/DF-CNN-IQA](https://github.com/Skythianos/DF-CNN-IQA)), accessed on 28 November 2021. This research received no external funding. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The datasets used were obtained from publically open source datasets from: 1. CLIVE: [[https://live.ece.utexas.edu/research/ChallengeDB/index.html](https://live.ece.utexas.edu/research/ChallengeDB/index.html)]([https://live.ece.utexas.edu/research/ChallengeDB/index.html](https://live.ece.utexas.edu/research/ChallengeDB/index.html)) (accessed on 28 November 2021), 2. KonIQ-10k: [[http://database.mmsp-kn.de/koniq-10k-database.html](http://database.mmsp-kn.de/koniq-10k-database.html)]([http://database.mmsp-kn.de/koniq-10k-database.html](http://database.mmsp-kn.de/koniq-10k-database.html)) (accessed on 28 November 2021), 3. SPAQ: [[https://github.com/h4](https://github.com/h4) nwei/SPAQ]([https://github.com/h4](https://github.com/h4) nwei/SPAQ) (accessed on 28 November 2021), 4. TID2013: [[http://www.ponomarenko.info/tid2013.htm](http://www.ponomarenko.info/tid2013.htm)]([http://www.ponomarenko.info/tid2013.htm](http://www.ponomarenko.info/tid2013.htm)) (accessed on 28 November 2021). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **PLCC** & **SROCC** & **KROCC** \\ \hline BLINDER [66] & 0.748 & 0.730 & 0.503 \\ DeepRN [67] & 0.746 & 0.725 & 0.481 \\ BLINDS-II [14] & 0.107 & 0.090 & 0.063 \\ BMPRI [68] & 0.453 & 0.389 & 0.298 \\ BRISQUE [15] & 0.509 & 0.460 & 0.310 \\ CurveIdeA [11] & 0.496 & 0.505 & 0.347 \\ DIVINE [9] & 0.479 & 0.434 & 0.299 \\ ENIQA [69] & 0.428 & 0.386 & 0.272 \\ GRAD-LOG-CP [70] & 0.427 & 0.384 & 0.261 \\ MultiGAP-NRIQA [36] & 0.841 & 0.813 & 0.585 \\ MSP-IQA [140] & 0.764 & 0.749 & 0.552 \\ NBIQA [71] & 0.503 & 0.509 & 0.284 \\ OG-IQA [73] & 0.442 & 0.427 & 0.289 \\ SSEQ [74] & 0.270 & 0.256 & 0.170 \\ UNQUE [57] & 0.842 & 0.826 & 0.589 \\ \hline CONTRIQUE [76] & - & 0.731 & - \\ DB-CNN [77] & - & 0.755 & - \\ DeepFL-IQA [78] & - & 0.704 & - \\ KonCep512 [79] & 0.848 & 0.825 & - \\ PaQ-2-PQ [36] & - & - & - \\ PQR [81] & - & 0.770 & - \\ WSP [43] & 0.840 & 0.820 & - \\ \hline DF-CNN-IQA & **0.854** & **0.831** & **0.598** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cross-database test. Methods were trained on KonIQ-10k [4] and tested on CLIVE [50]. The best results are typed in **bold**, and the second-best ones are underlined. We denoted by ‘-’ if the data were not available.