## 2 Related Work  
### Literature Review  
As VR contents are increasingly used in many applications, research on the creation and processing of the contents is also growing. In addition, 360-degree image and video acquisition sensors are now easily accessible for consumers, which has gained the attention of researchers for use in generating high-quality images. [PERSON] et al. [18] introduced an interesting study generating panoramas from a two-dimensional sketch and proposed a spherical generative adversarial network (GAN) system for stitching. The sketch, a concise geometric structure comprising about 7% of the panoramic image, created a high-fidelity spherical image. [PERSON] et al. [19] introduced a compact approach to capture high-quality omnidirectional panorama images with motion parallax. They captured a 360-degree video using a handheld 360-degree video camera with a selfie stick by rotating it for 3 or 10 s. They improved the visual quality by improving the vertical distortion using deformable proxy geometry. [PERSON] et al. [20] proposed a GANs network to stitch a 360-degree image from its parts. They used a two-stage approach with series-parallel dilated convolution layers. The result showed many distortions in the output 360-degree image, as their approach is the initial step to generate a 360-degree image from an unseen area input. Similarly, [PERSON] et al. [21] focused on cost effectiveness and proposed a low-cost mono and stereo panorama automatic stitching system. For a mono omnidirectional image, they presented a sensor kit mounted on a drone consistingof six cameras. For a stereo omnidirectional image, they proposed a cost-effective visual sensor kit consisting of three cameras. For both systems, they developed their own stitching software to achieve a good-quality image from both the objective and subjective perspective. [PERSON] et al. (2018) presented a tool to create omnidirectional, synthetic, and photorealistic images in various projection formats such as equirectangular, cylindrical, dioptric, and equiangular with labeled information. The tool supports the generation of datasets with depth and semantic information of the panoramas. These panoramas are synthesized from a set of captures in a realistic virtual environment. Their depth and semantic information can be used to train learning algorithms and test 3D vision approaches. Parallel with the generation of panoramic contents, the processing of such content has also drawn great attention. Researchers and developers process such content for different purposes. [PERSON] et al. (2018) investigated the behavior of users while watching 360-degree videos and proposed a novel dataset of users' trajectories. They captured the viewing trajectories of users using twelve 360-degree videos recorded on computers/laptops and compared these computer-based trajectories with the existing head-mounted device (HMD). It is stated that users have almost the same behavior and navigation when wearing an HMD as when they watch 360-degree videos. [PERSON] et al. (2018) studied a quality enhancement of 360-degree images using deep neural networks based on GANs without changing the image resolution. They designed a compact network employing a multi-frequency structure with compressed Residual-in-Residual Dense Blocks and convolution layers from each dense block. [PERSON] et al. (2018) proposed a saliency predictor for panoramas to improve the perceptual experience of the viewer by predicting head and eye movements. They extracted features at different frequency bands and orientations using spherical harmonics in the spherical domain and used these features to predict head and eye movements and estimate the saliency. [PERSON] et al. (2018) researched coding efficiency. They used the features of weighted Craster parabolic projection PSNR and proposed an algorithm for residual weighting to reduce the residual magnitude for 360-degree video compression.