## 4 Experimental Results
### Impact of the Number of Fixation Points  
As mentioned above, the number of patches extracted per image is fixed by the number of fixation points. Its impact on the performance was here analyzed by varying the number of fixation points from 10 to 200. For each value of the number of fixation points, PCC and SROCC values were calculated. Figure 6 shows the correlation coefficients obtained on the CID:IQ dataset by splitting the database according to the reference images. The test set was composed of one-fold (20% of the reference image and its degraded versions), while the training-validation set included the remaining images. The latter was split randomly without overlapping (80% for training and then 20% for validation). This protocol ensures non-overlap or redundancy (in terms of image content) between sets. This procedure was repeated five times and the correlations were calculated by concatenating the scores. As expected, the lower the number of fixation points, the lower the correlation. Indeed, the number of fixation points fixes the amount of data of the training set, which directly impacts the capacity of our model to learn the data. Correlations of the two viewing distances were close and increased with the number of fixation points. The best performance was raised for number of fixation points = 180 (i.e., 180 patches extracted per image). In the following, the quality of each image is thus predicted through 180 patches, where the quality is the average of the quality for each of the 180 patches. The attention of an observer can be influenced by the viewing distance or the distortions [88], and as the incorporated scanpath predictor does not account for this, it may have an influence and should be investigated in future work. Figure 5: Samples of the VDID2014 dataset. In order to better show the relevance of our pipeline, we compared the proposed saliency-based patch selection with the classical approach (i.e., no selection) and a random selection. These tests were carried out using the modified version of VGG16 and the baseline model, which corresponds to the modified version of VGG16 without integrating the viewing distance (i.e., one input and one output). The latter was trained using subjective scores of one viewing distance (i.e., 2.5*H) and tested on two distances (i.e., 2.5*H and 5*H). This procedure was applied to not associate several outputs to a single input. Table 2 shows the correlations obtained on CID:IQ dataset. As can be seen, a random selection of patches provided poorer results, while the proposed saliency-based patch selection improved the performance. Compared to the random selection, the use of all patches (i.e., no selection) improved the performance, but the global correlation still lower than that achieved with the proposed saliency-based patch selection. In addition, the integration of the viewing distance as input (i.e., proposed model) to the baseline model highly improved the performance whatever the selection type, especially when the proposed saliency-based selection step was applied. Figure 7 shows the loss values obtained in the training and validation sets across the number of epochs for one random splitting of the CID:IQ dataset. The loss values of both sets decreased until stabilizing, indicating no-overfitting. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**50 cm (2.5*H)**} & \multicolumn{2}{c}{**100 cm (5*H)**} & \multicolumn{2}{c}{**ALL**} \\ \hline
**Selection** & **PCC** & **SROCC** & **PCC** & **SROCC** & **PCC** & **SROCC** \\ \hline \multicolumn{5}{c}{**Without integration of the viewing distance (baseline model)**} \\ \hline
**Random** & 0.670 & 0.667 & 0.637 & 0.621 & 0.641 & 0.629 \\ \hline
**No** & **0.725** & **0.736** & 0.664 & 0.659 & 0.681 & 0.682 \\ \hline
**Saliency** & 0.712 & 0.718 & **0.705** & **0.704** & **0.695** & **0.695** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Impact of the patch selection step.