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Conference papers

User-Adaptive Rotational Snap-Cutting for Streamed 360° Videos

Miguel Fabian Romero Rondon 1 Lucile Sassatelli 1, 2, 3 Ramon Aparicio-Pardo 1 Frédéric Precioso 4, 5, 6 
5 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , UNS - Université Nice Sophia Antipolis (1965 - 2019), JAD - Laboratoire Jean Alexandre Dieudonné, Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Designing editing cuts for cinematic Virtual Reality (VR) has been under active investigation. Recently, the connection has been made between cuts in VR and adaptive streaming logics for 360 • videos, with the introduction of rotational snap-cuts. Snap-cuts can benefit the user's experience both by improving the streamed quality in the FoV and ensuring the user sees important elements for the plot. However, snap-cuts should not be too frequent and may be avoided when not beneficial to the streamed quality. We formulate the dynamic decision problem of snap-change triggering as a model-free Reinforcement Learning. We express the optimum cut triggering decisions computed offline with dynamic programming and investigate possible gains in quality of experience compared to baselines. We design Imitation Learning-based dynamic triggering strategies, and show that only knowing the past user's motion and video content, it is possible to outperform the controls without and with all cuts.
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Contributor : Lucile Sassatelli Connect in order to contact the contributor
Submitted on : Friday, February 5, 2021 - 1:21:15 AM
Last modification on : Sunday, May 1, 2022 - 3:16:57 AM
Long-term archiving on: : Thursday, May 6, 2021 - 6:01:15 PM


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Miguel Fabian Romero Rondon, Lucile Sassatelli, Ramon Aparicio-Pardo, Frédéric Precioso. User-Adaptive Rotational Snap-Cutting for Streamed 360° Videos. EUROGRAPHICS Workshop on Intelligent Cinematography and Editing, May 2020, Norrkoping, Sweden. ⟨10.2312/wiced.20201125⟩. ⟨hal-02615978⟩



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