多维矢量矩阵变换域的运动矢量估计

桑爱军; 孙敏; 陈绵书; 钟江江; 陈贺新

doi:10.3788/CO.20150802.0191

多维矢量矩阵变换域的运动矢量估计

doi: 10.3788/CO.20150802.0191

cstr: 32171.14.CO.20150802.0191

吉林大学通信工程学院，吉林长春 130022

基金项目: 吉林省自然科学基金资助项目(No.20130101045JC);吉林省科技发展计划国际科技合作资助项目(No.20130413053GH);国家自然科学基金资助项目(No.61171078)

详细信息

通讯作者:
桑爱军(1973—)，女，山东莱州人，博士，教授，硕士生导师，1994年于华中理工大学获得学士学位，1997年于大连理工大学获得硕士学位，2002年于吉林大学获得博士学位，主要从事数字图像处理等方面的研究。E-mail:sangaj@jlu.edu.cn

中图分类号: TP941.1
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出版历程
- 收稿日期: 2014-12-11
- 录用日期: 2015-02-18
- 刊出日期: 2015-04-25

Motion vector estimation in multi-dimensional vector matrix transform domain

College of Communication Engineering,Jilin University,Changchun 130022,China

摘要

摘要: 为了实现对视频中运动目标的运动矢量估计,建立了多维矢量矩阵变换域运动估计系统,在变换域对运动视频中运动目标形成的多维能量集中平面进行研究。首先,介绍了多维矢量矩阵理论、变换理论以及运动目标在变换域形成能量集中平面的理论推导;然后,采用平面拟合的方法,求取运动矢量的大小;最后,分析对比了几种方法的效果和迭代速度。实验结果表明:多维矢量矩阵变换域的运动平面拟合方法估计的运动矢量精度达到10^-2 pixel,提供了一种变换域运动矢量估计的高精度方法。
- 视频编码 /
- 多维矢量矩阵 /
- 运动估计 /
- 迭代拟合
Abstract: To realize the motion vector estimation targeted at moving objects in video, the motion estimation system based on transformation domain of multi-dimensional vector matrix is established. The research is then carried out in transformation domain to study on multi-dimensional energy concentration plane formed by moving objects in moving videos. Firstly, the theory of multi-dimensional vector matrix and transformation are introduced. Then the theoretical derivation of energy concentration plane in transformation domain formed by moving objects is presented. The next step is to calculate the motion vector using plane fitting method. Finally,the performance and iteration speed among a few methods are analyzed. Experimental results show that the motion vector accuracy obtained by moving plane fitting in multi-dimensional vector matrix transformation domain can reach 10^-2 pixel. A new high accuracy motion vector estimation method in transformation domain is provided.
- video coding /
- multi-dimensional vector matrix /
- motion estimation /
- iterative fitting

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图 1 运动估计的框图

Figure 1. Block diagram of MVM-DCT

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图 2 均匀平移图像序列的前8帧

Figure 2. First 8 frames of image sequence formed by uniform translation

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图 3 变换后系数

Figure 3. Coefficients after transformation

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图 4 迭代拟合的流程图

Figure 4. Iterative fitting flowchart

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图 5 两种运动矢量的折叠面

Figure 5. Folding surface of two kinds of motion vector

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图 6 平面拟合效果图

Figure 6. Renderings of plane fitting

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图 7 剔除数据后平面拟合效果图

Figure 7. Renderings of plane fitting after excluding data

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图 8 进一步剔除数据后平面拟合效果图

Figure 8. Renderings of plane fitting after further excluding data

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图 9 迭代拟合试验结果

Figure 9. Iterative fitting test results

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表 1 迭代拟合试验结果

Table 1. Iterative fitting test results

精度(pixel) 1 2 3 4 9

运动矢量(2,0) 0.17 0.09 0.08 0.07 0.03

运动矢量(3,0) 0.10 0.04 0.03 0.02 0.02

H.264国际标准 0.25

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