SAW: Semantic-Aware WebRTC Transmission Using Diffusion-Based Scalable Video Coding

As video transmission systems expand into various complex scenarios, real-time video coding methods are essential for maintaining low latency and high perceptual quality across varying network conditions. In this work, we propose service-aware Web real-time communication (WebRTC), a semantic-assisted WebRTC system built on scalable video coding (SVC). Specifically, this system is structured with three layers: 1) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{1}$ </tex-math></inline-formula> extracts and down-samples semantic information at the encoder, employing a novel super-resolution (SR) method named BUS-DDIM at the decoder to enhance the transmission efficiency and machine vision recognition rate; 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> adaptively compresses high-quality video by discarding frames with little motion at the encoder to address latency issues under poor network conditions, and utilize the adjacent frame-guided denoised interpolation model called the adjacent frame-guided denoised diffusion implicit model for restoring the video; and 3) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{3}$ </tex-math></inline-formula> transmits high-quality video tailored for users with high-definition video requirements and favorable network conditions. These layers dynamically enhance the visual experience and ensure low latency across various network environments. Experiments are conducted on diverse videos to validate the effectiveness of the proposed framework. The performance evaluation under real-time scenarios indicates significant enhancements in video quality and transmission efficiency, showcasing compatibility and versatility across various applications.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-10-28
DOI
https://doi.org/10.1109/jiot.2024.3486725
Citations
5
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
1.25

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article

SAW: Semantic-Aware WebRTC Transmission Using Diffusion-Based Scalable Video Coding

Yihan Wen, Chung Shue Chen, Zheng Zhang, Jiayi Sun et al.
5 citations
IEEE Internet of Things Journal
Advanced Steganography and Watermarking Techniques
1.25
article

SAW: Semantic-Aware WebRTC Transmission Using Diffusion-Based Scalable Video Coding

Yihan Wen, Chung Shue Chen, Zheng Zhang, Jiayi Sun, Jinglei Li
article en
5 citations

Abstract

As video transmission systems expand into various complex scenarios, real-time video coding methods are essential for maintaining low latency and high perceptual quality across varying network conditions. In this work, we propose service-aware Web real-time communication (WebRTC), a semantic-assisted WebRTC system built on scalable video coding (SVC). Specifically, this system is structured with three layers: 1) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{1}$ </tex-math></inline-formula> extracts and down-samples semantic information at the encoder, employing a novel super-resolution (SR) method named BUS-DDIM at the decoder to enhance the transmission efficiency and machine vision recognition rate; 2) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> adaptively compresses high-quality video by discarding frames with little motion at the encoder to address latency issues under poor network conditions, and utilize the adjacent frame-guided denoised interpolation model called the adjacent frame-guided denoised diffusion implicit model for restoring the video; and 3) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{3}$ </tex-math></inline-formula> transmits high-quality video tailored for users with high-definition video requirements and favorable network conditions. These layers dynamically enhance the visual experience and ensure low latency across various network environments. Experiments are conducted on diverse videos to validate the effectiveness of the proposed framework. The performance evaluation under real-time scenarios indicates significant enhancements in video quality and transmission efficiency, showcasing compatibility and versatility across various applications.

IEEE Internet of Things JournalVol. 12(5)
Xidian University (CN), Dalian University of Technology (CN), Nokia (France) (FR)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 19%
Advanced Steganography and Watermarking Techniques
1.25
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