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Task-Oriented Video Compressive Streaming for Real-Time Semantic Segmentation
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AI
DOI：10.1109/TMC.2024.3446185.png)
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Abstract
En 中文
Real-time semantic segmentation (SS) is a major task for various vision-based applications such as self-driving. Due to the limited computing resources and stringent performance requirements, streaming videos from camera-embedded mobile devices to edge servers for SS is a promising approach. While there are increasing efforts on task-oriented video compression, most SS-applicable algorithms apply more uniform compression, as the sensitive regions are less obvious and concentrated. Such processing results in low compression performance and significantly limits the capacity of edge servers supporting real-time SS. In this paper, we propose STAC, a novel task-oriented DNN-driven video compressive streaming algorithm tailed for SS, to strike accuracy-bitrate balance and adapt to time-varying bandwidth. It exploits DNN's gradients as sensitivity metrics for fine-grained spatial adaptive compression and includes a temporal adaptive scheme that integrates spatial adaptation with predictive coding. Furthermore, we design a new bandwidth-aware neural network, serving as a compatible configuration tuner to fit time-varying bandwidth and content. STAC is evaluated in a system with a commodity mobile device and an edge server with real-world network traces. Experiments show that STAC can save up to 63.7-75.2% of bandwidth or improve accuracy by 3.1-9.5% compared to state-of-the-art algorithms, while capable of adapting to time-varying bandwidth.
Keywords:
Image coding
Bandwidth
Streaming media
Semantic segmentation
Accuracy
Servers
Predictive coding
Adaptive streaming
DNN-driven compression
edge computing
semantic segmentation
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