# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 3 Experimental Results and Analysis
### Parameter Study  
First, we conducted experiments to determine which Inception module in GoogLeNet [31] or in Inception-V3 [32] is the most appropriate for visual feature extraction to predict perceptual image quality. Second, we answer the question whether the concatenation of different Inception modules' feature vectors improves the prediction's performance or not. Third, we demonstrate that fine-tuning of the base CNN architecture results in significant performance increase. In this parameter study, we used KonIQ-10k database to answer the above-mentioned questions and to find the most effective design choices. As presented in the next subsection, our best proposal was used to carry out a comparison to the state-of-the-art using other databases as well. The results of the parameter study are summarized in Tables 1-4. Specifically, Tables 1 and 3 contain the results with GoogLeNet [31] and Inception-V3 [32] base architectures without fine-tuning, respectively. On the other hand, Tables 2 and 4 summarize the results when fine-tuning is applied. In these tables, we report the average, median, and standard deviation of the PLCC and SROCC values obtained after 20 random train-validation-test splits using KonIQ-10k database. Furthermore, we report the effectiveness of deep features extracted from different Inception modules. Moreover, the tables also contain the prediction performance of the concatenated deep feature vector. From these results, it can be concluded that the deep features extracted from the early Inception modules perform slightly poorer than those of intermediate and last Inception modules. Although most state-of-the-art methods [15; 18; 22] utilize the features of the last CNN layers, it is worth examining earlier layers as well, because the tables indicate that the middle layers encode the information that is the most powerful for perceptual quality prediction. We can also assert that feature vectors containing both mid-level and high-level deep representations are significantly more efficient than those of containing only one level's feature representation. Finally, it can be clearly seen that fine-tuning the base CNN architectures also improves the effectiveness of the extracted deep features. Overall, the deeper Inception-V3 [32] provides more effective features than GoogLeNet [31]. Our best proposal relies on Inception-V3 and concatenates the features of all Inception modules. In the following, we call this architecture _MultiGAP-NRIQA_ and compare it to other state-of-the-art in the next subsection. Another contribution of this parameter study may be the following. It is worth studying the features of different layers separately because the features of intermediate layers may provide a better representation of the given task than high-level features. Furthermore, the proposed feature extraction method may be also superior in other problems where the task is to predict one value only from the image data itself relying on a large enough database. In our environment (MATLAB R2019a, PC with 8-core i7700K CPU and two NVidia Geforce GTX 1080), the computational times of the proposed MultiGAP-NRIQA method are the following. The loading of the base CNN and the \(1024\times 768\)-sized or the \(512\times 384\) input image takes about \(1.8s\). Furthermore, the feature extraction from multiple Inception modules of Inception-V3 [32] and concatenation takes on average \(1.355\)s or \(0.976\)s on the GPU, respectively. Furthermore, the SVR regression takes \(2.976\)s on average computing on the CPU.