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

Visual Distortions in 360-degree Videos.

Omnidirectional (or 360°) images and videos are emergent signals being used in many areas, such as robotics and virtual/augmented reality. In particular, for virtual reality applications, they allow an immersive experience in which the user can interactively navigate through a scene with three degrees of freedom, wearing a head-mounted display. Current approaches for capturing, processing, delivering, and displaying 360° content, however, present many open technical challenges and introduce several types of distortions in the visual signal. Some of the distortions are specific to the nature of 360° images and often differ from those encountered in classical visual communication frameworks. This paper provides a first comprehensive review of the most common visual distortions that alter 360° signals going through the different processing elements of the visual communication pipeline. While their impact on viewers' visual perception and the immersive experience at large is still unknown-thus, it is an open research topic-this review serves the purpose of proposing a taxonomy of the visual distortions that can be encountered in 360° signals. Their underlying causes in the end-to-end 360° content distribution pipeline are identified. This taxonomy is essential as a basis for comparing different processing techniques, such as visual enhancement, encoding, and streaming strategies, and allowing the effective design of new algorithms and applications. It is also a useful resource for the design of psycho-visual studies aiming to characterize human perception of 360° content in interactive and immersive applications

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

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