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


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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hal-02615978 , version 1 (05-02-2021)



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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