Retina-based quality assessment of tile-coded 360-degree videos

Abstract

Nowadays, omnidirectional content, which delivers 360-degree views of scenes, is a significant aspect of Virtual Reality systems. While 360 video requires a lot of bandwidth, users only see visible tiles, therefore a large amount of bitrate can be saved without affecting the user’s experience on the service. The fact leads to current video adaptation solutions to filter out superfluous parts and extraneous bandwidth. To form a good basis for these adaptations, it is necessary to understand human’s video quality perception. In our research, we contribute to building an effective omnidirectional video database that can be applied to study the effects of the five zones of the human retina. We also design a new video quality assessment method to analyze the impacts of those zones of a 360 video according to the human retina. The proposed scheme is found to outperform 22 current objective quality measures by 11 to 31% in terms of the PCC parameter.

Efficient Deep Learning-Based Viewport Estimation for 360-Degree Video Streaming

“…Assume P(t0) is the position of the viewport at time t0. A viewport's center point can be located by using the longitude and latitude values of the viewport [24,25]. Figure 3 illustrates how spherical video captures a scene from every angle.…”