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TiCMP: A lightweight and efficient Tiled Cubemap projection strategy for Immersive Videos in Web-based players

Published:12 June 2018Publication History

ABSTRACT

The encoding, delivery and interactive consumption of omnidirectional videos still face many challenges. Traditional encoding techniques, based on Equirectangular Projection (ERP) formats, introduce significant pixel redundancy. This has prompted the appearance of advanced solutions based on the segmentation in separate regions or tiles, and their selective delivery depending on the users' viewpoint. However, tiling techniques introduce further challenges. First, neighboring pixels are encoded separately, which may result in noticeable separations between regions. Second, they can involve synchronization problems when the users' viewpoints change. Third, they may require further extensions to existing technologies, such as Dynamic Adaptive Streaming over HTTP (DASH), which makes their adoption in current web browsers very challenging. The use of Cubemap Projection (CMP) is alternatively gaining popularity due to its advantages compared to ERP. However, it requires the streaming of the whole 360° area. This paper proposes a novel tiled Cubemap (TiCMP) strategy that overcomes all the mentioned limitations. TiCMP is based on dividing the cube into two tiles, adaptively streaming them based on the users' viewpoint, and playing them out in a synchronized manner in web-based players. Evaluation results demonstrate that TiCMP provides significant bandwidth savings, without negatively impacting the Quality of Experience (QoE) when compared to traditional Equirectangular- and Cubemap-based strategies.

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  1. TiCMP: A lightweight and efficient Tiled Cubemap projection strategy for Immersive Videos in Web-based players

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    • Published in

      cover image ACM Conferences
      NOSSDAV '18: Proceedings of the 28th ACM SIGMM Workshop on Network and Operating Systems Support for Digital Audio and Video
      June 2018
      84 pages
      ISBN:9781450357722
      DOI:10.1145/3210445

      Copyright © 2018 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 12 June 2018

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      Overall Acceptance Rate118of363submissions,33%

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