Spherical clustering of users navigating 360{\deg} content

In Virtual Reality (VR) applications, understanding how users explore the omnidirectional content is important to optimize content creation, to develop user-centric services, or even to detect disorders in medical applications. Clustering users based on their common navigation patterns is a first direction to understand users behaviour. However, classical clustering techniques fail in identifying these common paths, since they are usually focused on minimizing a simple distance metric. In this paper, we argue that minimizing the distance metric does not necessarily guarantee to identify users that experience similar navigation path in the VR domain. Therefore, we propose a graph-based method to identify clusters of users who are attending the same portion of the spherical content over time. The proposed solution takes into account the spherical geometry of the content and aims at clustering users based on the actual overlap of displayed content among users. Our method is tested on real VR user navigation patterns. Results show that our solution leads to clusters in which at least 85% of the content displayed by one user is shared among the other users belonging to the same cluster.Comment: 5 pages, conference (Published in: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model

Omnidirectional video enables spherical stimuli with the $360 \times 180^ \circ$ viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video.Comment: Accepted by ACM MM 201

Exploring the impact of 360° movie cuts in users' attention

Virtual Reality (VR) has grown since the first devices for personal use became available on the market. However, the production of cinematographic content in this new medium is still in an early exploratory phase. The main reason is that cinematographic language in VR is still under development, and we still need to learn how to tell stories effectively. A key element in traditional film editing is the use of different cutting techniques, in order to transition seamlessly from one sequence to another. A fundamental aspect of these techniques is the placement and control over the camera. However, VR content creators do not have full control of the camera. Instead, users in VR can freely explore the 360° of the scene around them, which potentially leads to very different experiences. While this is desirable in certain applications such as VR games, it may hinder the experience in narrative VR. In this work, we perform a systematic analysis of users'' viewing behavior across cut boundaries while watching professionally edited, narrative 360° videos. We extend previous metrics for quantifying user behavior in order to support more complex and realistic footage, and we introduce two new metrics that allow us to measure users'' exploration in a variety of different complex scenarios. From this analysis, (i) we confirm that previous insights derived for simple content hold for professionally edited content, and (ii) we derive new insights that could potentially influence VR content creation, informing creators about the impact of different cuts in the audience's behavior