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)

Streaming and User Behaviour in Omnidirectional Videos

Omnidirectional videos (ODVs) have gone beyond the passive paradigm of traditional video, offering higher degrees of immersion and interaction. The revolutionary novelty of this technology is the possibility for users to interact with the surrounding environment, and to feel a sense of engagement and presence in a virtual space. Users are clearly the main driving force of immersive applications and consequentially the services need to be properly tailored to them. In this context, this chapter highlights the importance of the new role of users in ODV streaming applications, and thus the need for understanding their behaviour while navigating within ODVs. A comprehensive overview of the research efforts aimed at advancing ODV streaming systems is also presented. In particular, the state-of-the-art solutions under examination in this chapter are distinguished in terms of system-centric and user-centric streaming approaches: the former approach comes from a quite straightforward extension of well-established solutions for the 2D video pipeline while the latter one takes the benefit of understanding users’ behaviour and enable more personalised ODV streaming

Understanding User Navigation in Immersive Experience: an Information-Theoretic analysis

To cope with the large bandwidth and low-latency requirements, Virtual Reality (VR) systems are steering toward user-centric systems in which coding, streaming, and possibly rendering are personalized to the final user. The success of these user-centric VR systems mainly relies on the ability to anticipate viewers navigation. This has motivated a large attention in studying the prediction of user's movements in a VR experience. However, most of these work lack of a proper and exhaustive behavioural analysis in a VR scenario, leaving many key-behavioural questions unsolved and unexplored: Can some users be more predictable than others? Do users have their own way of navigating and how much is this affected by the video content features? Can we quantify the similarity of users navigation? Answering these questions is a crucial step toward the understanding of user's behaviour in VR; and it is the overall goal of this paper. By studying VR trajectories across different contents and through information-theoretic tools, we aim at characterizing navigation patterns both for each single viewer (profiling individually viewers - intra-user analysis) and for a multitude of viewers (identifying common patterns among viewers - inter-user analysis). For each of these proposed behavioural analyses, we describe the applied metrics and key observations that can be extrapolated

Extending 3-DoF Metrics to Model User Behaviour Similarity in 6-DoF Immersive Applications

Immersive reality technologies, such as Virtual and Augmented Reality, have ushered a new era of user-centric systems, in which every aspect of the coding-delivery-rendering chain is tailored to the interaction of the users. Understanding the actual interactivity and behaviour of the users is still an open challenge and a key step to enabling such a user-centric system. Our main goal is to extend the applicability of existing behavioural methodologies for studying user navigation in the case of 6 Degree-of-Freedom (DoF). Specifically, we first compare the navigation in 6-DoF with its 3-DoF counterpart highlighting the main differences and novelties. Then, we define new metrics aimed at better modelling behavioural similarities between users in a 6-DoF system. We validate and test our solutions on real navigation paths of users interacting with dynamic volumetric media in 6-DoF Virtual Reality conditions. Our results show that metrics that consider both user position and viewing direction better perform in detecting user similarity while navigating in a 6-DoF system. Having easy-to-use but robust metrics that underpin multiple tools and answer the question "how do we detect if two users look at the same content?" open the gate to new solutions for a user-centric syste

Extending 3-DoF Metrics to Model User Behaviour Similarity in 6-DoF Immersive Applications

Immersive reality technologies, such as Virtual and Augmented Reality, have ushered a new era of user-centric systems, in which every aspect of the coding--delivery--rendering chain is tailored to the interaction of the users. Understanding the actual interactivity and behaviour of the users is still an open challenge and a key step to enabling such a user-centric system. Our main goal is to extend the applicability of existing behavioural methodologies for studying user navigation in the case of 6 Degree-of-Freedom (DoF). Specifically, we first compare the navigation in 6-DoF with its 3-DoF counterpart highlighting the main differences and novelties. Then, we define new metrics aimed at better modelling behavioural similarities between users in a 6-DoF system. We validate and test our solutions on real navigation paths of users interacting with dynamic volumetric media in 6-DoF Virtual Reality conditions. Our results show that metrics that consider both user position and viewing direction better perform in detecting user similarity while navigating in a 6-DoF system. Having easy-to-use but robust metrics that underpin multiple tools and answer the question ``how do we detect if two users look at the same content?" open the gate to new solutions for a user-centric system

Do Users Behave Similarly in VR? Investigation of the User Influence on the System Design

With the overarching goal of developing user-centric Virtual Reality (VR) systems, a new wave of studies focused on understanding how users interact in VR environments has recently emerged. Despite the intense efforts, however, current literature still does not provide the right framework to fully interpret and predict users’ trajectories while navigating in VR scenes. This work advances the state-of-the-art on both the study of users’ behaviour in VR and the user-centric system design. In more detail, we complement current datasets by presenting a publicly available dataset that provides navigation trajectories acquired for heterogeneous omnidirectional videos and different viewing platforms—namely, head-mounted display, tablet, and laptop. We then present an exhaustive analysis on the collected data to better understand navigation in VR across users, content, and, for the first time, across viewing platforms. The novelty lies in the user-affinity metric, proposed in this work to investigate users’ similarities when navigating within the content. The analysis reveals useful insights on the effect of device and content on the navigation, which could be precious considerations from the system design perspective. As a case study of the importance of studying users’ behaviour when designing VR systems, we finally propose a user-centric server optimisation. We formulate an integer linear program that seeks the best stored set of omnidirectional content that minimises encoding and storage cost while maximising the user’s experience. This is posed while taking into account network dynamics, type of video content, and also user population interactivity. Experimental results prove that our solution outperforms common company recommendations in terms of experienced quality but also in terms of encoding and storage, achieving a savings up to 70%. More importantly, we highlight a strong correlation between the storage cost and the user-affinity metric, showing the impact of the latter in the system architecture design

Understanding user interactivity for the next-generation immersive communication: design, optimisation, and behavioural analysis

Recent technological advances have opened the gate to a novel way to communicate remotely still feeling connected. In these immersive communications, humans are at the centre of virtual or augmented reality with a full sense of immersion and the possibility to interact with the new environment as well as other humans virtually present. These next-generation communication systems hide a huge potential that can invest in major economic sectors. However, they also posed many new technical challenges, mainly due to the new role of the final user: from merely passive to fully active in requesting and interacting with the content. Thus, we need to go beyond the traditional quality of experience research and develop user-centric solutions, in which the whole multimedia experience is tailored to the final interactive user. With this goal in mind, a better understanding of how people interact with immersive content is needed and it is the focus of this thesis. In this thesis, we study the behaviour of interactive users in immersive experiences and its impact on the next-generation multimedia systems. The thesis covers a deep literature review on immersive services and user centric solutions, before develop- ing three main research strands. First, we implement novel tools for behavioural analysis of users navigating in a 3-DoF Virtual Reality (VR) system. In detail, we study behavioural similarities among users by proposing a novel clustering algorithm. We also introduce information-theoretic metrics for quantifying similarities for the same viewer across contents. As second direction, we show the impact and advantages of taking into account user behaviour in immersive systems. Specifically, we formulate optimal user centric solutions i) from a server-side perspective and ii) a navigation aware adaptation logic for VR streaming platforms. We conclude by exploiting the aforementioned behavioural studies towards a more in- interactive immersive technology: a 6-DoF VR. Overall in this thesis, experimental results based on real navigation trajectories show key advantages of understanding any hidden patterns of user interactivity to be eventually exploited in engineering user centric solutions for immersive systems

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise

Complexity measurement and characterization of 360-degree content

The appropriate characterization of the test material, used for subjective evaluation tests and for benchmarking image and video processing algorithms and quality metrics, can be crucial in order to perform comparative studies that provide useful insights. This paper focuses on the characterisation of 360-degree images. We discuss why it is important to take into account the geometry of the signal and the interactive nature of 360-degree content navigation, for a perceptual characterization of these signals. Particularly, we show that the computation of classical indicators of spatial complexity, commonly used for 2D images, might lead to different conclusions depending on the geometrical domain use

A new challenge: Behavioural analysis of 6-DoF user when consuming immersive media

Thanks to recent advances in computer graphics, wearable technology, and connectivity, Virtual Reality (VR) has landed in our daily life. A key novelty in VR is the role of the user, which has turned from merely passive to entirely active. Thus, improving any aspect of the coding-delivery-rendering chain starts with the need for understanding user behaviour. To do so, we investigate the navigation trajectories of users within a 6-Degrees-of-Freedom (DoF) VR environment. Specifically, we investigate the main differences and similarities between 3 and 6-DoF navigation through existing methodologies adopted to study user behaviour in 3-DoF settings. Our simulation results, based on real navigation paths of users while displaying dynamic volumetric media in 6-DoF conditions, show the limitations of clustering algorithms for 3-DoF in assessing user similarity in 6-DoF. Given these observations, we state the need for developing new solutions for the analysis of 6-DoF trajectories