Complexity measurement and characterization of 360-degree content

Simone, F.; Gutiérrez, Jesús; Callet, Patrick Le

doi:10.2352/issn.2470-1173.2019.12.hvei-216

Cited by 12 publications

(8 citation statements)

References 23 publications

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“…1. As it can be seen, SI and TI have been computed in three different projections, i.e., equirectangular (ER), cube-map (CM) and spherical (SP), to account for possible inaccuracies due to projection distortions [9], [99], [100]. Although small differences can be observed, the three domains' computations are highly correlated and show a wide distribution of spatial and temporal properties of the dataset.…”

Section: B Test Stimulimentioning

confidence: 99%

Subjective Evaluation of Visual Quality and Simulator Sickness of Short 360$^\circ$ Videos: ITU-T Rec. P.919

Gutiérrez¹,

Pérez

Orduna

et al. 2022

IEEE Trans. Multimedia

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Recently an impressive development in immersive technologies, such as Augmented Reality (AR), Virtual Reality (VR) and 360°video, has been witnessed. However, methods for quality assessment have not been keeping up. This paper studies quality assessment of 360°video from the cross-lab tests (involving ten laboratories and more than 300 participants) carried out by the Immersive Media Group (IMG) of the Video Quality Experts Group (VQEG). These tests were addressed to assess and validate subjective evaluation methodologies for 360°video. Audiovisual quality, simulator sickness symptoms, and exploration behavior were evaluated with short (from 10 seconds to 30 seconds) 360°sequences. The following factors' influences were also analyzed: assessment methodology, sequence Manuscript received

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Section: B Test Stimulimentioning

confidence: 99%

Subjective Evaluation of Visual Quality and Simulator Sickness of Short 360$^\circ$ Videos: ITU-T Rec. P.919

Gutiérrez¹,

Pérez

Orduna

et al. 2022

IEEE Trans. Multimedia

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“…In particular, low value of entropy stands for users focused all on a restricted area (i.e., focused content -high correlation among users); while high value means more exploratory movements (i.e., exploratory content -low correlation among users). Moreover, authors in [12] have applied this metric to omnidirectional images providing its validity also for this kind of content. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…On the contrary, UAI tends towards 0 when participants experience highly scattered navigation patterns, and they cannot be clustered together. In the Appendix A, we compare our proposed metric for detecting similarities in users' behaviour over time with a well-known metric (i.e., entropy of saliency map [12]). Fig.…”

Section: Looking For Users' Similaritiesmentioning

confidence: 99%

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

Rossi

Özçinar

Smolić

et al. 2020

ACM Trans. Multimedia Comput. Commun. Appl.

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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 details, we complement current datasets by presenting a public 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 an 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 maximises the user's experience. This is posed while taking into account network dynamics, type of video content, but also user population interactivity. Experimental results prove that our solution outperforms commonly company recommendations in terms of experienced quality but also in terms of encoding and storage, achieving a saving 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.

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“…To enable such user-centric systems, there is the need to understand users' interactivity models [1,2]. User movement in VR environments has been analysed, for both 3-DoF [3][4][5] and 6-DoF [6][7][8][9] scenarios, in terms of total and averaged interaction time and angular velocity, among others. User navigation in 6-DoF scenarios was also studied in the past in the context of locomotion and display technology for CAVE environments [10,11].…”

Section: Introductionmentioning

confidence: 99%

A New Challenge: Behavioural Analysis Of 6-DOF User When Consuming Immersive Media

Rossi

Viola

Toni

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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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.

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Complexity measurement and characterization of 360-degree content

Cited by 12 publications

References 23 publications

Subjective Evaluation of Visual Quality and Simulator Sickness of Short 360$^\circ$ Videos: ITU-T Rec. P.919

Subjective Evaluation of Visual Quality and Simulator Sickness of Short 360$^\circ$ Videos: ITU-T Rec. P.919

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

A New Challenge: Behavioural Analysis Of 6-DOF User When Consuming Immersive Media

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