Optimized Viewport Dependent Streaming of Stereoscopic Omnidirectional Video

Naik, Deepa; Curcio, Igor D. D.; Toukomaa, Henri

doi:10.1145/3210424.3210437

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…Moreover, the network response-based rate adaption also ensures improved video quality when adjusted based on overall traffic variations. Naik et al [115] performed two subjective experiments by smartly employing asymmetric qualities for both background and foreground views of stereoscopic videos. The authors demonstrate that the proposed strategy could save up to 15% and 41% bitrate for both background and foreground tiles, respectively.…”

Section: B Viewport-dependent Streamingmentioning

confidence: 99%

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Yaqoob

Muntean

2020

IEEE Commun. Surv. Tutorials

152

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Omnidirectional or 360 • video is increasingly being used, mostly due to the latest advancements in immersive Virtual Reality (VR) technology. However, its wide adoption is hindered by the higher bandwidth and lower latency requirements than associated with traditional video content delivery. Diverse researchers propose and design solutions that help support an immersive visual experience of 360 • video, primarily when delivered over a dynamic network environment. This paper presents the state-of-the-art on adaptive 360 • video delivery solutions considering end-to-end video streaming in general and then specifically of 360 • video delivery. Current and emerging solutions for adaptive 360 • video streaming, including viewport-independent, viewport-dependent, and tile-based schemes are presented. Next, solutions for network-assisted unicast and multicast streaming of 360 • video content are discussed. Different research challenges for both on-demand and live 360 • video streaming are also analyzed. Several proposed standards and technologies and top international research projects are then presented. We demonstrate the ongoing standardization efforts for 360 • media services that ensure interoperability and immersive media deployment on a massive scale. Finally, the paper concludes with a discussion about future research opportunities enabled by 360 • video.

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Section: B Viewport-dependent Streamingmentioning

confidence: 99%

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Yaqoob

Muntean

2020

IEEE Commun. Surv. Tutorials

152

View full text Add to dashboard Cite

show abstract

“…al. [23] assessed asymmetric video applied separately to the foreground and background views of omnidirectional sessions, i.e., applying asymmetric stereoscopic streaming delivery on the foreground view can save up to 41% bit rate, and using the same technique on the background view, one can save approximately up to 15% bit rate. Zhou et.…”

Section: Related Workmentioning

confidence: 99%

Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming

Griwodz

Jeppsson

Espeland

et al. 2019

Int. J. Semantic Computing

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360∘ panorama video displayed through Virtual Reality (VR) glasses or large screens offers immersive user experiences, but as such technology becomes commonplace, the need for efficient streaming methods of such high-bitrate videos arises. In this respect, the attention that 360∘ panorama video has received lately is huge. Many methods have already been proposed, and in this paper, we shed more light on the different trade-offs in order to save bandwidth while preserving the video quality in the user’s field-of-view (FoV). Using 360∘ VR content delivered to a Gear VR head-mounted display with a Samsung Galaxy S7 and to a Huawei Q22 set-top-box, we have tested various tiling schemes analyzing the tile layout, the tiling and encoding overheads, mechanisms for faster quality switching beyond the DASH segment boundaries and quality selection configurations. In this paper, we present an efficient end-to-end design and real-world implementation of such a 360∘ streaming system. Furthermore, in addition to researching an on-demand system, we also go beyond the existing on-demand solutions and present a live streaming system which strikes a trade-off between bandwidth usage and the video quality in the user’s FoV. We have created an architecture that combines RTP and DASH, and our system multiplexes a single HEVC hardware decoder to provide faster quality switching than at the traditional GOP boundaries. We demonstrate the performance and illustrate the trade-offs through real-world experiments where we can report comparable bandwidth savings to existing on-demand approaches, but with faster quality switches when the FoV changes.

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“…Moreover, there are research works that combines both solutions by encoding the viewport dependent tiles at a higher bitrate and proving scalable support to further enhance the Quality of Experience (QoE) of users [39], [40]. Furthermore, multi-layer streaming system with base and enhancement layers have also been studied [5], [36], [41]. Although tiled based coding systems are useful for streaming purposes, reducing transmission delays, providing higher flexibility and improving the QoE, the associated coding losses remain an issue.…”

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confidence: 99%

Deep Learning and Bidirectional Optical Flow Based Viewport Predictions for 360° Video Coding

2022

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The rapid development of virtual reality applications continues to urge better compression of 360 • videos owing to the large volume of content. These videos are typically converted to 2-D formats using various projection techniques in order to benefit from ad-hoc coding tools designed to support conventional 2-D video compression. Although recently emerged video coding standard, Versatile Video Coding (VVC) introduces 360 • video specific coding tools, it fails to prioritize the user observed regions in 360 • videos, represented by the rectilinear images called the viewports. This leads to the encoding of redundant regions in the video frames, escalating the bit rate cost of the videos. In response to this issue, this paper proposes a novel 360 • video coding framework for VVC which exploits user observed viewport information to alleviate pixel redundancy in 360 • videos. In this regard, bidirectional optical flow, Gaussian filter and Spherical Convolutional Neural Networks (Spherical CNN) are deployed to extract perceptual features and predict user observed viewports. By appropriately fusing the predicted viewports on the 2-D projected 360 • video frames, a novel Regions of Interest (ROI) aware weightmap is developed which can be used to mask the source video and introduce adaptive changes to the Lagrange and quantization parameters in VVC. Comprehensive experiments conducted in the context of VVC Test Model (VTM) 7.0 show that the proposed framework can improve bitrate reduction, achieving an average bitrate saving of 5.85% and up to 17.15% at the same perceptual quality which is measured using Viewport Peak Signal-To-Noise Ratio (VPSNR).

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Optimized Viewport Dependent Streaming of Stereoscopic Omnidirectional Video

Cited by 14 publications

References 14 publications

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

A Survey on Adaptive 360° Video Streaming: Solutions, Challenges and Opportunities

Efficient Live and On-Demand Tiled HEVC 360 VR Video Streaming

Deep Learning and Bidirectional Optical Flow Based Viewport Predictions for 360° Video Coding

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