In the last few years, the popularity of immersive applications has experienced a major increase because of the introduction of powerful imaging and display devices. The most popular immersive media are 360-degree videos, which provide the sensation of immersion. Naturally, these videos require significantly more data, which is a challenge for streaming applications. In this work, our goal is to design a perceptually efficient streaming protocol based on edited versions of the original content. More specifically, we propose to use visual attention and semantic analysis to implement an automatic perceptual edition of 360-degree videos and design an efficient Adaptive Bit Rate (ABR) streaming scheme. The proposed scheme takes advantage of the fact that movies are made of a sequence of different shots, separated by cuts. Cuts can be used to attract viewer’s attention to important events and objects. In this paper, we report the first stage of this scheme: the content analysis used to select temporal and spatial candidate cuts. For this, we manually selected candidate cuts from a set of 360-degree videos and analyzed the users' quality of experience (QoE). Then, we computed their salient areas and analyzed if these areas are good candidates for the video cuts.
Visual distortions in processed 360-degree visual content and consumed through head-mounted displays (HMDs) are perceived very differently when compared to traditional 2D content. To better understand how compression-related artifacts affect the overall perceived quality of 360-degree videos, this paper presents a subjective quality assessment study and analyzes the performance of objective metrics to correlate with the gathered subjective scores. In contrast to previous related work, the proposed study focuses on the equiangular cubemap projection and includes specific visual distortions (blur, blockiness, H.264 compression, and cubemap seams) on both monoscopic and stereoscopic sequences. The objective metrics performance analysis is based on metrics computed in both the projection domain and the viewports, which is closer to what the user sees. The results show that overall objective metrics computed on viewports are more correlated with the subjective scores in our dataset than the same metrics computed in the projection domain. Moreover, the proposed dataset and objective metrics analysis serve as a benchmark for the development of new perception-optimized quality assessment algorithms for 360-degree videos, which is still a largely open research problem.