# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 3 Experimental Results and Analysis
### Comparison to the State-of-the-Art  
To compare our proposed method to other state-of-the-art algorithms, we collected _seven_ traditional learning-based NR-IQA metrics (BIQI [45], BLIINDS-II [3], BRISQUE [6], CORNIA [46], DIIVINE [2], HOSA [47], and SSEQ [4]), _four_ deep learning based methods (BosICIP [15], CNN [14], DIQaM-NR [48], and WaDIQaM-NR [48]), and _one_ opinion-unaware method (PIQE [49]) whose original source code are available. Moreover, we reimplemented the deep learning based DeepBIQ [18] method. Furthermore, we added the FR-IQA metric SSIM to our comparison. Overall, we compared our proposed method--_MultiGAP-NRIQA_--to 14 other state-of-the-art IQA algorithms or metrics. The results can be seen in Table 5.  
To ensure a fair comparison, these traditional and deep methods were trained, tested, and evaluated exactly the same as our proposed method. Specifically, \(\sim\)60% of the images were used for training, \(\sim\)20% for validation, and \(\sim\)20% for testing purposes. If a validation set was not required, the training set contained \(\sim\)80% of the images. To compare our method to the state-of-the-art, we report the average PLCC and SROCC values of 20 random train-validation-test splits of our method and those of other algorithms. As already mentioned, the results are summarized in Table 5. More specifically, this table illustrates the measured average PLCC and SROCC on the three largest available IQA databases (Table 6 summarizes the major parameters of the IQA databases used in this paper). As one can see from the results, our proposed approach outperformed other state-of-the-art methods on all three benchmark IQA databases. On the large KonIQ-10k [33] and KADID-10k [34] databases, the difference between our results and those of other methods is more apparent than on the smaller TID2013 [35] databases. More specifically, our architecture is able to outperform the state-of-the-art on KonIQ-10k [33] and KADID-10k [34] even without fine-tuning. Furthermore, the difference between our method and other state-of-the-art algorithms is somewhat larger on KADID-10k than on KonIQ-10k because the applied fine-tuning process is more effective on artificial distortions than on authentic ones. We attribute this improvement to the fact that our method extracts resolution independent features from images at different levels of abstraction in contrast to previous deep approaches. Furthermore, test images are not broken into patches in our approach during the prediction phase in contrast to other deep methods, such as DeepBIQ [18], DIQaM-NR [48], or WaDIQaM-NR [48]. Although the advantage of the proposed feature extraction method is less significant on TID2013 [35], our method still preserves its first place. Furthermore, the performance of the proposed method without fine-tuning achieves the state-of-the-art on TID2013 [35] as well. Specifically, our method without fine-tuning outperforms two deep methods (DIQaM-NR [48] and WaDIQaM-NR [48]) and seven traditional methods which generally perform significantly better on the smaller TID2013 than on KonIQ-10k or KADID-10k. The scatter plots showing the predicted MOS