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J4  2014, Vol. 48 Issue (3): 423-429    DOI: 10.3785/j.issn.1008-973X.2014.03.007
计算机技术,无线电电子学     
基于经验模式分解和多种评价准则的电子稳像
於俊1,2,汪增福1,2,3
1.中国科学技术大学 语音及语言信息处理国家工程实验室 安徽 合肥 230027;
2.中国科学技术大学 自动化系,安徽 合肥 230027;3.中国科学院 合肥智能机械研究所,安徽 合肥 230031
Video stabilization based on empirical mode decomposition and
several evaluation criterions
YU Jun1,2, WANG Zeng-fu1,2,3
1.National Laboratory of Speech and Language Information Processing, University of Science and Technology of China,
Hefei 230027, China;2.Department of Automation, University of Science and Technology of China, Hefei 230027, China;
3.Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031,China
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摘要:

针对摄影中易产生视频抖动的问题,提出一个实时鲁棒的视频去抖动系统.该系统具有如下特性:1)提取参考帧和当前帧的Sift特征点,并对它们进行匹配,通过随机采样一致(RANSAC)算法来得到全局运动参数;2)基于经验模式分解以及多种评价准则(全局运动参数和特征点对位置误差)来确定抖动参数以实现对当前帧的运动补偿;3)结合图像纹理合成算法来修复运动补偿后的视频帧,从而得到稳定和完整的输出视频.通过比较抖动视频和去抖动后的视频结果表明:该系统能够在保持实时性的同时提高视频的平均信噪比约7.2 dB,大大提高人对视频中内容的辨识度和观察舒适感.

Abstract:

In view of the probtem of video jitter in photograph, a real-time and robust video stabilization system was proposed. It has following characteristics: 1)feature points are matched between reference frame and current frame with Sift feature extraction, and global motion parameters are obtained  with random sample consensus (RANSAC) algorithm|2)motion compensation is applied to current frame with jittered parameters obtained by empirical mode decomposition and several evaluation criterions (global motion parameters and location errors of corresponding feature points); 3)stable and complete video is obtained after each frame is repaired with texture synthesis. Comparing between jitter video and stabilized video, the experimental results confirm that the system can increase the average peak signal-to-noise ratio around 7.2 dB in real-time, thus can increase the ability of identification and perceptive comfort on video content.

出版日期: 2018-06-10
:  TP 18  
基金资助:

 国家自然科学基金资助项目(61303150);安徽自主创新专项资金智能语音技术研发和产业化专项基金资助项目(13z02008);中央高校基本科研业务费专项资金青年创新基金资助项目(WK2100100020);中国博士后科学基金资助项目(2012M521248).

通讯作者: 汪增福,男,教授.     E-mail: yujun888@mail.ustc.edu.cn
作者简介: 於俊(1983-),男,副研究员,主要从事人机接口、智能机器人等研究. E-mail:harryjun@ustc.edu.cn
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引用本文:

於俊,汪增福. 基于经验模式分解和多种评价准则的电子稳像[J]. J4, 2014, 48(3): 423-429.

YU Jun, WANG Zeng-fu. Video stabilization based on empirical mode decomposition and
several evaluation criterions. J4, 2014, 48(3): 423-429.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2014.03.007        http://www.zjujournals.com/eng/CN/Y2014/V48/I3/423

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