This paper presents a novel spherical convolutional neural network based scheme for
saliency detection for 360 videos. Specifically, in our spherical convolution neural network …

Traditional 2D image-based saliency prediction models suffer from unsatisfactory
performance when dealing with stereoscopic 3D (S3D) images because eye movements in …

360 media allows observers to explore the scene in all directions. The consequence is that
the human visual attention is guided by not only the perceived area in the viewport but also …

Virtual reality (VR) applications have become popular recently and rapidly commercialized.
The behaviors of users watching 360-degree omni-directional videos have not been fully …

The rapid development of virtual reality applications continues to urge better compression of
360° videos owing to the large volume of content. These videos are typically converted to 2 …

For 360 VR video, the user's viewport is portion, but the video needs to be sent to 360°
spheres. So 360 VR video requires large bandwidth. In this paper, we propose a viewport …

The prediction of the visual saliency of omnidirectional images in VR is valuable for
understanding visual behaviors. However, the equipment cost, software setups, hardware …

A 360° video stream provide users a choice of viewing one's own point of interest inside the
immersive contents. Performing head or hand manipulations to view the interesting scene in …

Understanding of visual attention is crucial for omnidirectional video (ODV) viewed for
instance with a head-mounted display (HMD), where only a fraction of an ODV is rendered …

The growing interest in omnidirectional videos (ODVs) that capture the full field-of-view
(FOV) has gained 360-degree saliency prediction importance in computer vision. However …