# Multi-Pooled Inception Features for No-Reference Image Quality Assessment
## 3 Experimental Results and Analysis
### Parameter Study
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Dimension** & **PLCC** & **SROCC** \\ \hline _inception\_3a-output_ & 256 & 0.845/0.845\((\pm 0.006)\) & 0.842/0.841\((\pm 0.007)\) \\ _inception\_3b-output_ & 480 & 0.861/0.861\((\pm 0.007)\) & 0.856/0.858\((\pm 0.007)\) \\ _inception\_4a-output_ & 512 & 0.876/0.876\((\pm 0.004)\) & 0.872/0.872\((\pm 0.006)\) \\ _inception\_4b-output_ & 512 & 0.874/0.874\((\pm 0.005)\) & 0.865/0.864\((\pm 0.008)\) \\ _inception\_4c-output_ & 512 & 0.875/0.877\((\pm 0.006)\) & 0.865/0.865\((\pm 0.006)\) \\ _inception\_4d-output_ & 528 & 0.876/0.875\((\pm 0.007)\) & 0.864/0.864\((\pm 0.007)\) \\ _inception\_4e-output_ & 832 & 0.872/0.871\((\pm 0.006)\) & 0.861/0.862\((\pm 0.005)\) \\ _inception\_5a-output_ & 832 & 0.873/0.874\((\pm 0.005)\) & 0.859/0.860\((\pm 0.005)\) \\ _inception\_5b-output_ & 1024 & 0.861/0.861\((\pm 0.008)\) & 0.851/0.850\((\pm 0.008)\) \\ \hline _All concatenated_ & 5488 & **0.889/0.889**\((\pm 0.007)\) & **0.879/0.877**\((\pm 0.006)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of deep features extracted from GoogLeNet’s [31] Inception modules without fine-tuning measured on KonIQ-10k [33]. Average/median \((\pm std)\) values are reported over 20 random train-test splits. The best results are in **bold**. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Dimension** & **PLCC** & **SROCC** \\ \hline _inception\_3a-output_ & 256 & 0.850/0.849\((\pm 0.007)\) & 0.846/0.846\((\pm 0.007)\) \\ _inception\_3b-output_ & 480 & 0.866/0.866\((\pm 0.006)\) & 0.861/0.862\((\pm 0.007)\) \\ _inception\_4a-output_ & 512 & 0.881/0.881\((\pm 0.005)\) & 0.877/0.876\((\pm 0.006)\) \\ _inception\_4b-output_ & 512 & 0.877/0.876\((\pm 0.005)\) & 0.870/0.870\((\pm 0.006)\) \\ _inception\_4c-output_ & 512 & 0.879/0.880\((\pm 0.005)\) & 0.869/0.868\((\pm 0.005)\) \\ _inception\_4d-output_ & 528 & 0.880/0.880\((\pm 0.006)\) & 0.869/0.868\((\pm 0.005)\) \\ _inception\_4e-output_ & 832 & 0.877/0.877\((\pm 0.005)\) & 0.867/0.867\((\pm 0.007)\) \\ _inception\_5a-output_ & 832 & 0.878/0.878\((\pm 0.007)\) & 0.864/0.864\((\pm 0.007)\) \\ _inception\_5b-output_ & 1024 & 0.865/0.865\((\pm 0.007)\) & 0.856/0.856\((\pm 0.008)\) \\ \hline _All concatenated_ & 5488 & **0.894/0.894**\((\pm 0.006)\) & **0.884/0.884**\((\pm 0.006)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of deep features extracted from GoogLeNet’s [31] Inception modules with fine-tuning measured on KonIQ-10k [33]. Average/median \((\pm std)\) values are reported over 20 random train–validation–test splits. The best results are in **bold**. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Dimension** & **PLCC** & **SROCC** \\ \hline _mixed0_ & 256 & 0.843/0.843\((\pm 0.006)\) & 0.839/0.839\((\pm 0.006)\) \\ _mixed1_ & 288 & 0.848/0.848\((\pm 0.005)\) & 0.844/0.844\((\pm 0.005)\) \\ _mixed2_ & 288 & 0.849/0.849\((\pm 0.006)\) & 0.844/0.844\((\pm 0.007)\) \\ _mixed3_ & 768 & 0.861/0.860\((\pm 0.005)\) & 0.858/0.855\((\pm 0.006)\) \\ _mixed4_ & 768 & 0.897/0.897\((\pm 0.005)\) & 0.889/0.889\((\pm 0.006)\) \\ _mixed5_ & 768 & 0.906/0.906\((\pm 0.004)\) & 0.898/0.898\((\pm 0.005)\) \\ _mixed6_ & 768 & 0.902/0.901\((\pm 0.004)\) & 0.890/0.891\((\pm 0.006)\) \\ _mixed7_ & 768 & 0.884/0.884\((\pm 0.004)\) & 0.870/0.870\((\pm 0.006)\) \\ _mixed8_ & 1280 & 0.892/0.891\((\pm 0.004)\) & 0.879/0.879\((\pm 0.006)\) \\ _mixed9_ & 2048 & 0.871/0.871\((\pm 0.005)\) & 0.859/0.859\((\pm 0.006)\) \\ _mixed10_ & 2048 & 0.842/0.844\((\pm 0.006)\) & 0.828/0.829\((\pm 0.008)\) \\ \hline _All concatenated_ & 10,048 & **0.910/0.911**\((\pm 0.005)\) & **0.901/0.901**\((\pm 0.005)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of deep features extracted from Inception-V3’s [32] Inception modules without fine-tuning measured on KonIQ-10k [33]. Average/median \((\pm std)\) values are reported over 20 random train–test splits. The best results are in **bold**.