一种改进的混编视频水印方案

提出了一种具有鲁棒性和脆弱性的改进混编视频水印方案:将鲁棒性和脆弱性水印分别嵌入到DCT宏块的中频交流系数和运动矢量的水平分量中,实现了秘密信息及验证信息在视频中的隐藏,具有版权保护和视频完整性认证的能力.实验结果表明,与G.Qiu等提出的混编视频水印方案相比,该方案在鲁棒性水印嵌入时,码率增幅平均降低6％,有更好的抗攻击能力;脆弱性水印对攻击的敏感性更高.     

一种可伸缩性视频加密方案

随着网络的不断普及和发展,数字视频在网络中有了越来越多的应用,随之也提出了视频安全性问题,通过视频加密可以很好地解决此问题.为了满足网络对视频加密的要求,设计了一种可伸缩性视频加密的方案.考虑到小波的多尺度性,建立了一个基于小波变换的视频压缩平台,在此平台上进行了仿真加密实验,即在压缩的过程中从小波系数置乱、系数随机翻牌、运动矢量置乱等方面进行加密,并与其他算法进行了比较实验.实验结果表明,该方案具有加密效果好,密钥量较低,安全性好等优点,同时对压缩比和压缩效率有较好的保持,是一种高效的视频加密方案。     

一种实现视频水印嵌入的新方法

在研究前人提出的视频水印理论基础上,结合MPEG编解码方案和人眼视觉特性,采用在宏块的运动速度图中以十字方式嵌入水印的方法,该方法的优点是:水印的嵌入不会影响I帧的图像质量,水印的嵌入量大,不但提高了水印的不可见性,而且增强了水印的稳健性.     

一种基于运动矢量的视频水印算法

运动矢量可作为视频水印嵌入点,利用运动矢量的相角可嵌入水印信息．抖动调制根据水印住来调制量化区间,基于运动矢量的相角抖动调制依据水印信息位把运动矢量的相角调制相应的区间而嵌入水印信息．其水印嵌入算法简单,对视频质量和码流的影响小,并可实现水印的盲检测和盲提取。     

一种改进的降尺寸视频转码算法

在Diversity &; Importance架构的基础上提出一种改进的空间降尺度视频转码算法,利用方差作为转码目标宏块的运动矢量测度因子,并通过自适应阈值的分类方法对不同视频序列选择合适的重编码模式。该算法比传统的运动矢量合成方法提高了约1dB的PSNR,在保持较高转码图像质量的同时有效地控制了编码长度,其转码后码流长度只相当于AMVR-DIM方法产生码流的40%~95%。     

一种新的矢量量化水印算法

提出了一种新的矢量量化(VQ)数字图像水印算法.该算法所采用的矢量量化器基于关联的双码书结构,双码书分别是传统的基本码书和带标记位的新型扩展码书.在扩展码书中引入了表示水印信息位段的开始与结束标志,可以为每个水印像素动态地分配不等长的字符串.对于信道误差引起的含水印索引丢失,该算法具有良好的鲁棒性.同时,在嵌入以小像素值居多的非二值水印时,可以减少媒体图像块的需求,从而减少了因嵌入水印带来的额外失真.实验结果表明该算法具有良好的性能.     

一种改进的DWT-SVD域参考水印方案

针对一类SVD域图像水印方案存在过高虚警概率的问题,提出了一种改进的混合DWT和SVD的图像参考水印算法。算法先对载体图像进行[n]层的离散小波变换,然后随机选取第[n]层的部分或全部子带形成参考子带,并对参考子带进行SVD分解;将Arnold置乱处理后的水印嵌入到SVD分解后的奇异值矩阵中。实验表明,提出的算法具有较好的透明性和安全性;与其他方案相比,解决了虚警概率问题,且对于大部分的攻击,具有更好的鲁棒性能。     

基于运动矢量的抗几何攻击视频水印方案

许多数字图像和视频水印方案都无法抵挡几何变换攻击,即使是轻微的旋转、缩放、平移也将会破坏水印的同步,这就使得水印难以检测出来。为解决此难题,提出了一种新颖的视频水印算法。该算法利用了运动矢量特征的相对关系不易随几何失真变换而改变的特性,使得水印具有内在的抗几何失真的鲁棒性。该算法中,水印嵌入和检测时,不仅可根据平均运动矢量的方向和幅度确定水印模板的位置和方向,而且水印检测不需要原始视频。实验表明,该水印算法对几何变换攻击具有较强的鲁棒性。     

一种采用曲线分割的矢量图水印算法

针对矢量图形既需具有鲁棒性又要保证图形几何精度的问题,提出了一种针对矢量图形水印嵌入、提取和检测方法。水印的嵌入算法是将一幅矢量图形视为曲线的集合,按设定阈值进行曲线分割;再在容差范围内,使每条曲线对应一个水印位,且对曲线中每个结点嵌入一个含有用户证书信息的水印点。水印提取算法则对原图和含水印图进行对应的分割;按嵌入算法的逆过程逐曲线提取出水印信息;再通过计算原水印信息和所提取水印信息的相似度检测水印存在与否和符合程度。实验结果表明,该方法在无攻击和几何变形操作攻击下的水印检测相似度均接近100;且水印图即使在剪裁了的2/3后,仍能保证大于09的相似度;同时,方法具有抗D-P压缩能力,并可借助所给出的相似度阈值经验曲线,控制水印点的偏离角度,或在水印信息检测的自动化处理流程中设定参数,起到既能保证矢量图形的精度,又具有较好的鲁棒性的作用。     

基于运动矢量区域特征的视频水印方案

H.264是一种高压缩比、高视频质量、高复杂度的新一代视频压缩标准,因为其高压缩率的特性,一些常用的水印方案无法直接运用于该视频压缩标准。提出了一种利用运动矢量的区域特性来隐藏水印信息的视频水印方案,该算法的最大特点为简单、实用,并且在隐藏水印信息的过程中还可以降低H.264编码中运动估计部分的运算复杂度。实验结果表明,通过该算法对视频图像嵌入水印后,主观上无法分别图像区别,PSNR值的波动被有效的控制在±0.1范围内,码率的增加在2%左右,并且插入的水印信息能够被100%的正确提取,运动估计中的搜索点减少1/2。由此表明,该算法是一种简单、高效的视频水印隐藏方案,并且细化区域的分类,可以达到使隐藏的水印信息加倍的效果。     

一种自适应半脆弱数字水印算法

结合人眼视觉特性及数字图像的自相关性,提出一种具有自适应性的半脆弱数字水印算法。该算法采用改进的低通滤波器,对于中低频图像信息,通过动态的自适应调节中间像素灰度,改变两端的灰度差分来实现水印的嵌入。实验表明该算法对所有等级的JPEG压缩均具有很好的鲁棒性,并且对图像擦除、加噪、挤压等恶意篡改具有脆弱性、敏感性以及准确的检测定位能力。     

An algorithm to estimate mean vehicle speed from MPEG Skycam video

With low computation cost, motion vectors can be readily extracted from MPEG video streams and processed to estimate vehicle motion speed. A statistical model is proposed to model vehicle speed and noise. In order to achieve high estimation accuracy and also study the limitations of the proposed algorithm, we quantitatively evaluated four parameters used in our algorithm: temporal filter window size T, video resolution R _v (CIF/QCIF), motion vector frame distance m, and video bit-rates. Our experiments showed that the mean vehicle speed can be estimated with high accuracy, up to 85 to 92% by proper spatial and temporal processing. The proposed algorithm is especially suitable for Skycam-based application, where the traditional tracking-based or virtual-loop-based approaches perform poorly because of their requirements of high-resolution images. Although extensive work has been done in extracting motion information directly from MPEG video data in compressed domain, to our best knowledge, this paper is the very first work in which stationary motion (speed) of moving objects can be estimated with high accuracy directly from MPEG motion vectors. Furthermore the proposed method is not limited to vehicle speed estimation by nature and it can be applied to other applications where the stationary motion assumption is satisfied.

基于交流伺服控制单元MCW151的位置控制系统设计

文章介绍了欧姆龙公司最新推出的新型交流伺服控制单元MCW151的原理和性能，阐述了基于交流伺服控制单元MCW151的位置控制系统的特点，并且设计了一种新的基于MCW151的高精度伺服控制系统。     

Multiple initial point prediction based search pattern selection for fast motion estimation

A novel, computationally efficient and robust scheme for multiple initial point prediction has been proposed in this paper. A combination of spatial and temporal predictors has been used for initial motion vector prediction, determination of magnitude and direction of motion and search pattern selection. Initially three predictors from the spatio-temporal neighboring blocks are selected. If all these predictors point to the same quadrant then a simple search pattern based on the direction and magnitude of the predicted motion vector is selected. However if the predictors belong to different quadrants then we start the search from multiple initial points to get a clear idea of the location of minimum point. We have also defined local minimum elimination criteria to avoid being trapped in local minimum. In this case multiple rood search patterns are selected. The predictive search center is closer to the global minimum and thus decreases the effect of monotonic error surface assumption and its impact on the motion field. Its additional advantage is that it moves the search closer to the global minimum hence increases the computation speed. Further computational speed up has been obtained by considering the zero-motion threshold for no motion blocks. The image quality measured in terms of PSNR also shows good results.     

Adaptive robust estimation of affine parameters from block motion vectors

In this paper, we propose an affine parameter estimation algorithm from block motion vectors for extracting accurate motion information with the assumption that the undergoing motion can be characterized by an affine model. The motion may be caused either by a moving camera or a moving object. The proposed method first extracts motion vectors from a sequence of images by using size-variable block matching and then processes them by adaptive robust estimation to estimate affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a continuous weight function based on a Sigmoid function. During the estimation process, we tune the Sigmoid function gradually to its hard-limit as the errors between the model and input data are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. Experimental results show that the suggested approach is very effective in estimating affine parameters reliably.     

Temporal logic motion planning for dynamic robots

In this paper, we address the temporal logic motion planning problem for mobile robots that are modeled by second order dynamics. Temporal logic specifications can capture the usual control specifications such as reachability and invariance as well as more complex specifications like sequencing and obstacle avoidance. Our approach consists of three basic steps. First, we design a control law that enables the dynamic model to track a simpler kinematic model with a globally bounded error. Second, we built a robust temporal logic specification that takes into account the tracking errors of the first step. Finally, we solve the new robust temporal logic path planning problem for the kinematic model using automata theory and simple local vector fields. The resulting continuous time trajectory is provably guaranteed to satisfy the initial user specification.     

基于DC系数和运动矢量的快速场景分割算法

场景分割技术是动态视频分析和基于内容的视频检索的基础，以检测出来的场景作为基本单元，可以进一步对视频内容进行分析和建立索引．本文旨在提出一种基于MPEG压缩视频流的场景分割算法，利用MPEG数据流中已有的DCT DC系数和运动矢量，来检测场景的变换，从而实现场景分割，针对实际视频流中场景突变和渐变两类变换．本文提出两种方法分别处理不同情况，对于突变检测，该算法可以定位到帧，由于该算法进行最小程度的解码，降低了计算复杂度，因而大大提高了检测速度．     

一种基于梯度模型的MPEG压缩域的运动对象分割算法

视频数据大都是经过压缩域的形式存储和传输的,且直接在压缩域进行视频对象分割无需运动估计等复杂的计算,速度较快。本文提出了一种基于梯度模型的MPEG压缩域的运动对象分割算法。首先利用DCT(AC[1]和AC[8])系数获得所有物体的边缘,然后综合在累积运动矢量基础上得到的边缘运动信息,从而获得感兴趣运动物体的边缘。仿真实验结果表明,它可以取得满意的分割质量。     

Improving the selection of feature points for tracking

The problem considered in this paper is how to select the feature points (in practice, small image patches are used) in an image from an image sequence, such that they can be tracked adequately further through the sequence. Usually, the tracking is performed by some sort of local search method looking for a similar patch in the next image in the sequence. Therefore, it would be useful if we could estimate the size of the convergence region for each image patch. There is a smaller chance of error when calculating the displacement for an image patch with a large convergence region than for an image patch with a small convergence region. Consequently, the size of the convergence region can be used as a proper goodness measure for a feature point. For the standard Kanade-Lucas-Tomasi (KLT) tracking method, we propose a simple and fast way to approximate the convergence region for an image patch. In the experimental part, we test our hypothesis on a large set of real data.