中国科学院机构知识库网格系统: 检索

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	IVP: An Intelligent Video Processing Architecture for Video Streaming 期刊论文 OAI收割 IEEE TRANSACTIONS ON COMPUTERS, 2023, 卷号: 72, 期号: 1, 页码: 264-277 作者: Gao, Chengsi; Wang, Ying; Han, Yinhe; Chen, Weiwei; Zhang, Lei \| 收藏 \| 浏览/下载：13/0 \| 提交时间：2023/07/12 Video enhancement compressed video DNN approximate computing optical flow accelerator
	CBREN: Convolutional Neural Networks for Constant Bit Rate Video Quality Enhancement 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 7, 页码: 4138-4149 作者: Zhao, Hengrun; Zheng, Bolun; Yuan, Shanxin; Zhang, Hua; Yan, Chenggang \| 收藏 \| 浏览/下载：35/0 \| 提交时间：2022/12/07 Image coding Quantization (signal) Streaming media Bit rate Image restoration Transform coding Video recording Quality enhancement CBR compressed video dual-domain restoration
	Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 3, 页码: 1089-1102 作者: Hu, Juan; Liao, Xin; Wang, Wei; Qin, Zheng \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2022/06/06 Videos Information integrity Feature extraction Streaming media Faces Forensics Social networking (online) Video forensics compressed Deepfake videos frame-level stream temporality-level stream
	A video error concealment method using data hiding based on compressed sensing over lossy channel 期刊论文 OAI收割 TELECOMMUNICATION SYSTEMS, 2018, 卷号: 68, 期号: 2, 页码: 337-349 作者: Chen, Yanli; Wang, Hongxia; Wu, Hanzhou; Sun, Xingming \| 收藏 \| 浏览/下载：20/0 \| 提交时间：2019/12/16 Video error concealment Data hiding Compressed sensing Lossy channel
	Surveillance video synopsis in the compressed domain for fast video browsing 期刊论文 OAI收割 JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2013, 卷号: 24, 期号: 8, 页码: 1431-1442 作者: Wang, Shi-zheng; Wang, Zhong-yuan; Hu, Rui-min 收藏 \| 浏览/下载：26/0 \| 提交时间：2015/09/23 Surveillance video Compressed domain Video synopsis Video labeling Scalable browsing Fast browsing Background modeling Intelligent video
	A simple and fast moving object segmentation based on H.264 compressed domain information (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Computational and Information Sciences, ICCIS 2012, August 17, 2012 - August 19, 2012, Chongqing, China 作者: 收藏 \| 浏览/下载：14/0 \| 提交时间：2013/03/25 The paper presents a simple and fast approach for moving object segmentation based on H.264 compressed domain information for the application of indoor video surveillance with static camera. Due to the characteristics of indoor video surveillance the proposed method of segmentation avoids complicated background model like Gaussian Mixture background model. On the contrary it chooses some simple information like the type of Macroblock etc.. Experimental results of several specific H.264 compressed video sequences demonstrate the good segmentation quality of the proposed approach. 2012 IEEE.
	An intelligent video surveillance system (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on E-Product E-Service and E-Entertainment, ICEEE2010, November 7, 2010 - November 9, 2010, Henan, China Gao S. 收藏 \| 浏览/下载：15/0 \| 提交时间：2013/03/25 This paper presents an intelligent video surveillance system. The system is composed of one or more nodes flexibly according to the application scenarios such as private properties banks and museums. Each node is an autonomous vision-based device capable to perform intelligent tasks. It is able to digitize and compress the acquired analog video signals in MPEG-4 standard and then transmit the compressed video stream to the control center. At the same time the node makes use of a statistical approach for real-time detecting moving object and online alarm generation to enable a single human operator to monitor activities over a complex area using a distributed network of active video sensors. The node is implemented on a platform with high performance to ensure that the algorithm is able to run in real-time. Applications demonstrate that the intelligent system has an excellent performance in many scenarios to help people make decisions more accurately and rapidly. 2010 IEEE.