# Blind Image Quality Assessment with Deep Learning:
## 4 Experimental Results  
The results we obtained are not fully consistent with the results presented by the original authors. We believe this is mainly related to the physical setup that was used for the training process. More specifically, the setup impacted the maximum batch size that we could use in training. Consequently, the batch size affected the overall model performance, as we will detail next. Unlike the Google Colab infrastructure that the authors used in their original manuscript, our local machine could not fit the training process with a batch size greater than 8 (in the original paper a batch size of 16 was used). However, the results we obtained are close to the ones presented by the authors. We show results obtained with different batch sizes, which show its impact on the final model performance. We explore the impact of batch size on the training process and compared the results with the original KonCept512 pre-trained model. The authors reflected in [9] that training some CNN base architectures with very small batch sizes and much larger input sizes limits the best possible performance that could be achieved. Table 1 shows the results obtained by testing the trained models with batch sizes of eight, four, and two on KonIQ-10k training set and testing these model on KonIQ-10k and LIVEitW test set. We compare these results to the original KonCept512 model trained in [9]. The results show that the smaller the batch size, the lower the performance of the model. Thus, our intuition would be that using a batch size of 16 and the procedures described in [9] with the assumptions we made in the beginning of this study, the original KonCept512 model could be reproduced with the same performance. In addition to these models, we explored the use of a loss function that reflect the maximization of the PLCC. The PLCC-induced loss function used is [20]: \(loss=(1-plcc)/2\), where \(plcc\) is the Pearson Linear Correlation Coefficient obtained from the training process. As the objective of the model is to achieve the best prediction linearity between the predicted perceptual quality scores and the subjective quality scores, the PLCC-induced loss could feature linearity [21]. Although this loss function is not used in isolation [19; 20; 21], we explored its impact on the training and testing of models. Table 2 shows the results in KonIQ-10k and LIVEitW test set obtained using the model trained in KonIQ-10k training set with the PLCC-induced loss function. Comparing the results in Table 1 with these results, we note that we obtained a small improvement in the performance of the models, even if we reduce the batch size to four. However, when we monitor the MSE values of the models, we observe that these values are much higher than models trained with MSE loss function. Nonetheless, in addition to the improvement of the evaluation metrics, we note that training models with MSE loss are more unstable when varying the batch sizes [20]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Test on KonIQ-10k**} & \multicolumn{2}{c}{**Test on LIVE-itW**} \\ \hline
**Model** & **Batch Size** & **SROCC** & **PLCC** & **SROCC** & **PLCC** \\ \hline KonCept512 [9] & 16 & 0.921 & 0.937 & 0.825 & 0.848 \\ \hline \multirow{3}{*}{Current Study} & 8 & 0.889 & 0.903 & 0.800 & 0.809 \\  & 4 & 0.793 & 0.774 & 0.667 & 0.661 \\ \cline{1-1}  & 2 & 0.426 & 0.413 & 0.363 & 0.349 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Trained model with different batch size compared to the original pre-trained model KonCept512. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Test on KonIQ-10k**} & \multicolumn{2}{c}{**Test on LIVE-itW**} \\ \hline
**Loss Function** & **Batch Size** & **SROCC** & **PLCC** & **SROCC** & **PLCC** \\ \hline \multirow{3}{*}{PLCC} & 8 & 0.914 & 0.930 & 0.816 & 0.837 \\  & 4 & 0.871 & 0.877 & 0.748 & 0.713 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trained model with PLCC-induced loss function to maximize the PLCC with different batch size.