# Supervised Learning Based Peripheral Vision System for
## 2 Related Prior Research
In short, the approaches [4; 5; 6; 18; 19] used the computer vision algorithms to extend the context, while the proposed approach employs a deep learning-based approach to generate the context. Image completion approaches based on convolutional neural network (CNN) [20; 21; 22; 23; 24; 25; 26; 27] were also proposed in the literature to fill the hole in the image and recover the lost region. The approach for the deep learning-based context generation [3] satisfied the properties of the real-time performance and the naturalness at the same time, but the flickering problem disturbed the viewer's immersion. A similar approach [25; 27] removed the flickering problem proposing deep neural network and extended the research [3] to HMD videos. A recent approach based on Two-stage conditional GANS (generative adversarial network) [26], was presented in the literature to extend the image completion with the 360-degree image data. The computer vision-based context generation methods did not fulfill the properties of the real-time performance and the naturalness of the scene [3] simultaneously. These approaches lack the method to generate sufficient context images with the consideration of processing time and naturalness as compared to deep learning-based methods. The human fovea comprises 1% of the retina. It consists of a large number of photoreceptors for high resolution vision, while the density of the photo receptors reduces outside it, resulting in the coarser vision. Due to the property of deep neural networks (DNN) to generate plausible content, deep learning was utilized for the context generation as it has an analogy with human vision being acute in narrower region [3]. The research proposed by [PERSON] et al. [20] successfully generated the natural image in such a way that the user could not recognize it as 'filled' in first glance. The user can fill the peripheral imagery by applying the mask to the peripheral portion and considering it as 'hole' [3]. In this paper, we presented the peripheral vision system having the capability to create immersive visual display via extension of the video content using deep learning. The hardware of the peripheral vision system consists of the LED TV and the projector (see Table 2), while the software to extend the video content is PCE [22]. The original high-resolution video is played on LED TV, while the extended output of the deep neural network is projected using Digital projector to provide the immersive visual experiences to the user. The proposed research is different from the approaches [3; 25] as we employed PCE network for extending the video frame, while the ExtVision and Deep dive used the pix2 pix and 3D convolution-basednetwork, respectively. Our approach and the ExtVision tackled only the spatial correlation, while the deep dive also considered the temporal correlation in the deep neural network. Our approach provided the qualitative and quantitative evaluation of the extended content from DNN, while the ExtVision and Deep dive only performed the qualitative evaluation. Our work is closely related to the ExtVision [3] which has already reported video projection and context image generation using DNN network \"pix2 pix\". The research [3; 25] did not compare the testing results of their networks with the ground truth images in term of RMSE (Root Mean Square Error) or PSNR (Peak Signal to Noise Ratio) values. We have utilized PCE network for peripheral vision system (PCE was not previously used for peripheral vision system-based Video display application). We have performed the quantitative evaluation of the testing results with the ground truth data in term of RMSE and PSNR values. We investigated the effect of different sizes of the extended area on the network's accuracy and concluded that larger size of the extended area results in less PSNR value (see Table 3). This paper is an extended version of [PERSON] et al. [2], which presented the work on small video dataset of the Ocean, while this article utilized large Ocean video dataset.