Multiple Description Coding for Best-Effort Delivery of Light Field Video Using GNN-Based Compression

In recent years, Light Field (LF) video has grabbed much attention as an emerging form of immersive media. LF collects, through a lens matrix, light information emanating in every direction, and obtains rich information about the scene, providing users with an immersive 6 Degrees of Freedom (DoF) ex...

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Vydané v:IEEE transactions on multimedia Ročník 25; s. 690 - 705
Hlavní autori: Hu, Xinjue, Pan, Yuxuan, Wang, Yumei, Zhang, Lin, Shirmohammadi, Shervin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
best-effort video delivery
Coding
Decoding
Degrees of freedom
Encoding
graph neural network
Graph neural networks
Image coding
Light field video
multiple description coding
Packet loss
Scheduling
Spatial resolution
Streaming media
Three-dimensional displays
Video compression
Video transmission
ISSN:1520-9210, 1941-0077
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Shrnutí:In recent years, Light Field (LF) video has grabbed much attention as an emerging form of immersive media. LF collects, through a lens matrix, light information emanating in every direction, and obtains rich information about the scene, providing users with an immersive 6 Degrees of Freedom (DoF) experience. The visual content between different viewpoints is highly homogenized, suggesting the possibility of good compression and encoding. However, most fixed-structure LF coding schemes are difficult to adapt to the real-time requirements of different LF applications and best-effort network conditions causing packet loss. In this paper, we propose a dynamic adaptive LF video transmission scheme that can achieve high compression and yet provide near-distortion-free LF video when the network condition is stable. Additionally, for unstable network conditions a description scheduling algorithm is proposed, which can decode the LF video with the highest possible quality even if partial data cannot be received completely and/or timely. We achieve this by designing a Multiple Description Coding (MDC) based solution to transport the LF video compressed by a Graph Neural Network (GNN) model. Experimental results show that the scheduling algorithm can improve the quality of the decoding results by 3% to 15%. Compared with other similar schemes, our system greatly improves the reliability of the video streaming system against packet loss/error and supports heterogeneous receivers.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2021.3129918