基于中高位视频监控的图像及视频质量增强算法
作者单位:

清华大学 电子工程系

基金项目:

科技部国家重点研发计划变革性领域:智能通信架构与可信协议基础(2018YFA0701601)


Image and Video Quality Enhancement Algorithm Based on Medium and High Level Video Surveillance
Author:
Affiliation:

Department of Electronie Engineering, Tsinghua University

Fund Project:

National key research and development program

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    摘要:

    随着多媒体业务及移动网络的迅速发展,为实现智慧城市的数据互联互通,中高点位的监控设备、物联网、AI等技术让城市监控视频的数据采集、上传及整理自动化,有效地对城市安全风险进行主动发现、及时预警及提前干涉。然而,监控视频巨大的数据量给传输网络带来了极大的压力,针对此问题,本文提出一种基于语义特征提取的神经网络模型图像及视频质量增强算法。该算法首先提出了基于语义特征的图像恢复增强框架,然后建立退化模型和重建模型的联合优化。在公开数据集上对所提模型进行验证,并与现有算法进行对比,所提方法相比新型超分辨率算法PULSE(Photo Upsampling via Latent Space Exploration, 潜空间搜索照片升采样)能够实现得分50%的提升,并且和原始高清图像、视频质量得分接近。在用户评价方面,有81%的重建结果被认为优于对比算法,结果表明所提算法具有更高的重构图像和视频质量。

    Abstract:

    With the development of multimedia services and mobile networks, in order to achieve the data interconnection and interoperability of smart cities, medium and high point surveillance equipment, Internet of Things (IoT), AI and other technologies make the data collection, uploading and organization of urban surveillance video automated, effectively providing active discovery, timely warning and early intervention of urban security risks. However, the huge data volume of surveillance video puts huge pressure on the transmission networks, for this reason, this paper proposes a neural network model image and video quality enhancement algorithm based on semantic feature extraction. The algorithm firstly proposes a joint optimization of the degradation model and then reconstruction model to address the problems of existing image and video recovery enhancement methods. The proposed model is validated on a publicly available dataset and compared with existing algorithms, the proposed method can achieve a 50% improvement in score compared to PULSE method and is close to the original HD image and video quality. In terms of user evaluation, 81% of the reconstruction results were found to be superior to the comparison algorithm. The results show that the proposed algorithm has higher reconstructed image and video quality.

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引用本文

向涛,葛宁,宋奇蔚.基于中高位视频监控的图像及视频质量增强算法[J].南京信息工程大学学报,,():

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历史
  • 收稿日期:2024-07-05
  • 最后修改日期:2024-11-01
  • 录用日期:2024-11-01

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