{"title":"Task-Oriented Video Compressive Streaming for Real-Time Semantic Segmentation","authors":"Xuedou Xiao;Yingying Zuo;Mingxuan Yan;Wei Wang;Jianhua He;Qian Zhang","doi":"10.1109/TMC.2024.3446185","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10684013/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.