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LSTM-based viewpoint prediction for multi-quality tiled video coding in virtual reality streaming

Jamali, Mohammadreza, Coulombe, Stéphane, Vakili, Ahmad and Vazquez, Carlos. 2020. « LSTM-based viewpoint prediction for multi-quality tiled video coding in virtual reality streaming ». In IEEE International Symposium on Circuits and Systems (ISCAS) (Seville, Spain, Oct. 10-21, 2020) Institute of Electrical and Electronics Engineers.
Compte des citations dans Scopus : 14.

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Abstract

Virtual reality (VR) streaming is impaired by the large amount of data required to deliver 360-degree video resulting in low-quality end user experience when network bandwidth is limited, or latency is high. To address these challenges, proposed in this paper is a novel method for viewpoint prediction for long-term horizons in VR streaming. This method uses a long short-term memory (LSTM) encoder-decoder network to carry out a sequence-to-sequence prediction. To enhance the results obtained by this network, experiments are performed using viewpoint information from users on low-latency networks. By applying an effective tile-based quality assignment after viewpoint prediction, a 61% average bandwidth reduction, with respect to the transmission of the whole ERP frame, is achieved along with a high-quality viewport rendered to the end user.

Item Type: Conference proceeding
ISBN: 2158-1525
Professor:
Professor
Coulombe, Stéphane
Vázquez, Carlos
Affiliation: Génie logiciel et des technologies de l'information, Génie logiciel et des technologies de l'information
Date Deposited: 03 Jun 2021 13:35
Last Modified: 21 Dec 2022 15:32
URI: https://espace2.etsmtl.ca/id/eprint/22700

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