# Supervised Learning Based Peripheral Vision System for
## 4 Experiments and Results  
### PCE for Ocean and Forest Dataset  
In this paper, we proposed the system that generates the extended video content using deep learning treating the center of video as input and the outside area as output using different epoch models trained on the training dataset. In this research, we used seven videos of the Ocean scene at 30 fps. We used six videos as training dataset and one as testing dataset. The training and testing videos were resized to the resolution of 256 \(\times\) 256. We extracted the frames from the training and testing videos at 1 fps and 30 fps respectively. We trained the PCE architecture and the time for training the PCE architecture for 10 epochs was found to be 458 s. There was no risk of overfitting, and this is likely due to the low number of model parameters. The size of the minibatches varied depending on memory capabilities of the graphics card [22]. The parameters, steps, and conditions for this experiment are the same as for PCE architecture [22].  
For 256 \(\times\) 256 videos, we considered three cases of the pixel band around the video center, i.e., 32, 48, and 64 pixels, respectively, to investigate the effect of different size of the extended area on the network's accuracy. These pixel bands yield the video centers to be (192 \(\times\) 192), (160 \(\times\) 160), and (128 \(\times\) 128), respectively, shown in Figure 4. For different pixel bands, we separately trained and tested PCE. The purpose of this experiment is to compare the PSNR values for the three cases. This experiment concludes that larger the pixel band results in less PSNR value. One similar experiment is reported in the related research [26]. We trained the PCE architecture for 10 to 1000 epochs for three cases and the testing result on the test video for different epochs in term of RMSE and PSNR are shown in Figure 5. The best values of RMSE and PSNR for 32, 48, and 64 pixels band are recorded  
In another experiment, we used two different categories of datasets, i.e., Ocean and forest datasets for PCE with only 64 pixels band (extended area). We used four videos of Ocean dataset (94 K frames extracted at 2 fps) for training and the PCE is trained for 753 epochs (1 epoch = 4700 steps). We then tested the trained PCE model on the two videos of Ocean dataset (frames extracted at 2 fps). We repeated the training of PCE with Forest dataset with seven videos (123 K frames extracted at 2 fps) and the PCE is trained for 1000 epochs (1 epoch = 6155 steps). We tested the trained PCE model on the two videos of Forest dataset (frames extracted at 2 fps). The testing results for PCE for the Ocean and forest data sets are shown in Figure 7. The testing result of the Ocean dataset shows that the extended area is filled with the related content of the seabed. In Figure 7b, the generated image contains the environment of sea plants as compared to the rock or sand in Figure 7f. We performed the testing of the trained model for the frames extracted at 30 fps. So, it seems that Figure 7 shows the same input image twice, but these images (Figure 7g,h are different. Similarly, Figure 7c,d contains the rock content (in the middle of the bottom) as compared to the grass (plant) in Figure 7g,h. These results with 64 pixel band are similar to those reported in the related work [26]. The research [26] reports the generation of the tree-like objects despite the input being building and the reason for this result was small input area (large extended area). We also performed Demos in the testbed with the peripheral vision system for the Ocean and Forest dataset. Figure 8 shows the Demos in term of the images, while the Demo videos can be found in the links.  
Figure 6: The extension of the video frame using PCE for training videos (1 st row) and testing video (2 nd row), (**a,d**) Input video frame (**b,e**) Output video frame, and (**c,f**) Target video frame.