• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >SASRT: Semantic-Aware Super-Resolution Transmission for Adaptive Video Streaming over Wireless Multimedia Sensor Networks
【2h】

SASRT: Semantic-Aware Super-Resolution Transmission for Adaptive Video Streaming over Wireless Multimedia Sensor Networks

机译：SASRT：用于无线多媒体传感器网络上自适应视频流的语义感知超分辨率传输

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There are few network resources in wireless multimedia sensor networks (WMSNs). Compressing media data can reduce the reliance of user’s Quality of Experience (QoE) on network resources. Existing video coding software, such as H.264 and H.265, focuses only on spatial and short-term information redundancy. However, video usually contains redundancy over a long period of time. Therefore, compressing video information redundancy with a long period of time without compromising the user experience and adaptive delivery is a challenge in WMSNs. In this paper, a semantic-aware super-resolution transmission for adaptive video streaming system (SASRT) for WMSNs is presented. In the SASRT, some deep learning algorithms are used to extract video semantic information and enrich the video quality. On the multimedia sensor, different bit-rate semantic information and video data are encoded and uploaded to user. Semantic information can also be identified on the user side, further reducing the amount of data that needs to be transferred. However, identifying semantic information on the user side may increase the computational cost of the user side. On the user side, video quality is enriched with super-resolution technologies. The major challenges faced by SASRT include where the semantic information is identified, how to choose the bit rates of semantic and video information, and how network resources should be allocated to video and semantic information. The optimization problem is formulated as a complexity-constrained nonlinear NP-hard problem. Three adaptive strategies and a heuristic algorithm are proposed to solve the optimization problem. Simulation results demonstrate that SASRT can compress video information redundancy with a long period of time effectively and enrich the user experience with limited network resources while simultaneously improving the utilization of these network resources.
机译：无线多媒体传感器网络（WMSN）中的网络资源很少。压缩媒体数据可以减少用户的体验质量（QoE）对网络资源的依赖。现有的视频编码软件，例如H.264和H.265，仅专注于空间和短期信息冗余。但是，视频通常会长时间保留冗余。因此，在不损害用户体验和自适应传递的情况下，长时间压缩视频信息冗余是WMSN的挑战。本文提出了一种用于WMSN的自适应视频流系统（SASRT）的语义感知超分辨率传输。在SASRT中，一些深度学习算法用于提取视频语义信息并丰富视频质量。在多媒体传感器上，不同的比特率语义信息和视频数据被编码并上传到用户。语义信息也可以在用户侧进行标识，从而进一步减少了需要传输的数据量。但是，在用户侧识别语义信息可能会增加用户侧的计算成本。在用户方面，视频质量通过超分辨率技术得以丰富。 SASRT面临的主要挑战包括识别语义信息的位置，如何选择语义和视频信息的比特率以及应如何将网络资源分配给视频和语义信息。将优化问题表述为复杂度受限的非线性NP-hard问题。提出了三种自适应策略和一种启发式算法来解决优化问题。仿真结果表明，SASRT可以有效地长时间压缩视频信息冗余，并以有限的网络资源丰富用户体验，同时提高这些网络资源的利用率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号