面向视觉监控的视频压缩研究
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摘要
视觉监控通过分析来自一个或多个摄像机的信息，可以监视和控制大而复杂的空间上分布的区域。人们目前的研究主要集中在如何实现或代替人的视觉功能或实现尽可能的自动监控系统，针对监控视频的压缩研究相对较少。但事实上，人们仍然需要对数据量巨大的监控视频实现高效压缩，而且对于监控视频编码提出了新的特殊要求，此外监控系统内的视频压缩不是一个孤立的问题，而是一个需要进行预处理的系统问题。所以，针对监控视频的压缩研究很有意义。本文研究内容包括三条线索：第一、为了给监控员提供远程报警信号，同时启动本地视频压缩程序而进行的运动检测研究；第二、为了得到监控对象而进行的运动分割、以及其后对监控视频进行基于对象的视频编码研究；第三、针对一类特殊监控场合而进行的ROI视频编码研究。本文的研究内容梗概如下。
一、旨在提供报警信号和启动压缩程序的运动检测研究
因为相邻两帧存在运动会引起相同位置上DCT系数大小和符号的改变，所以通过比较DCT系数的符号就可以确定是否有运动对象出现。而M-JPEG的码流特点表明在压缩码流中可以获得每帧的DCT系数的符号。为了消除随机噪声带来的影响，将其引起DCT系数块符号变化数目模型化为高斯分布，以此确定运动块阈值。场景照度突然变化带来的大量虚假运动块数用中值滤波加以消除。最后以校准帧最大的运动块数作为确定当前帧是否存在运动的阈值。实验结果表明，在不同的量化矩阵、不同的运动块阈值下，均能实现正确的检测。
二、以运动物体作为监控对象的分割研究
将噪声分布近似为Gauss分布或Laplace分布，再设定误判概率进而确定一个阈值就可以利用假设检验的思想，构成统计量对不同对象判断其运动与否。以当前位置灰度差值d、模板内、以及分块内所有d的不同组合作为检验统计量，对当前位置像素、模板中心像素、分块进行判决。针对不同统计量和判决对象的实验结果表明，基于这两种分布的检验方法都能不同程度地对测试视频分割出较好的结果，但基于Laplace的检验方法的综合性能较优。实验结果还表明本文提出的“像素判决一中值滤波一宏块掩模”策略具有较好的健壮性。
三、基于增强区域的监控视频压缩研究
在本文提出的面向监控应用的基于对象的视频编码框架中，不含监控信息的背景只编码一次发送到接收端(融合在线更新机制)。对于监控对象区域，首先对其进行增强以达到所需的视觉效果。监控对象的形状信息表示为宏块坐标；监控对象之间的时间冗余通过运动估计来消除，但搜索范围限制在前一帧监控对象区域之内；由于增强处理使得监控区域的灰度级减少了，所以对运动估计的残差进行无损熵编码。实验结果表明，本文方案得到的重建视频帧能提供对比度增强的监控对象区域，同时又能达到可观的压缩效率。
为了适应本文的基于对象的监控视频编码，本文还提出了一种综合多种优点的自适应快速运动估计算法。包括搜索起点的预测、不同的搜索模式、中途停止技术、以及优先搜索机制。实验结果显示，本文的运动估计算法既能提供与其他算法可比的性能，又有很低的计算复杂度。
四、基于感兴趣区域(ROI：Region OfInterest)编码思想的监控视频压缩研究
针对摄像机固定监控区域固定的一类监控视频，本文以监控对象区域为ROI。首先将视频分为固定大小的帧组(GOF：Group OfFrames)，然后对GOF进行3D小波变换。每一帧内ROI设为矩形而且位置相同，由左上角和右下角坐标描述，在这两个角之间所有的样值都属于ROI掩模。3D ROI内的小波系数先被上移，然后使用3D SPIHT算法进行编码，使ROI得到比特资源的优先分配。实验表明，在不同的比特率下，监控区域总能得到优先的重建，获得较好的重建质量。
By analyzing information from one or several cameras, a visual surveillance system can supervise and control fields distributed in large and complex areas. The present main research concentrate on how to realize or substitute human vision, or achieve a surveillance system with automation as much as possible, while study on compression of surveillance video is fewer relatively. In fact, the efficient compression of surveillance video with huge data volume is needed yet. Furthermore, new requirement is proposed on surveillance video coding. In addition, video compression inside a surveillance system is not a single problem; instead, it is a system problem with video pre-processing. Therefore, it is important to study compression of surveillance video. This paper includes three cues: one, motion detection in order to provide an remote alarm signal for supervisors or an indication for the start of local compression procedure; the other, motion segmentation for acquisition of surveillance objects and thereafter object-based video coding; the third, ROI video coding in view of some special surveillance cases. The main ideas of this paper are outlined as follows.
1. Study of motion detection to provide an alarm signal and a start indication of video compression
Because motion between adjacent frames can cause change in terms of DCT coefficient amplitudes and signs, the appearance of motion objects can be determined by the change of signs of DCT coefficients. It is showed that signs of DCT coefficients can be acquired from the M-JPEG code stream. In order to eliminate the impact imposed by random noise, the number of DCT coefficient sign changes caused by the noise is modeled as Gauss distribution, which can determine a threshold of motion blocks. The large number of false motion blocks brought by abrupt change of scene luminance is removed by median filtering. At last, the biggest number of motion blocks of a frame in the calibration stage is used as a threshold of motion frames. Experiments show accurate motion detection can be achieved under different quantization matrixes and/or different motion blocks thresholds.
2. Study of segmentation of surveillance objects with motion
The camera noise statistical characteristic is approximated as Gauss or Laplace distribution, then an error probability is set and a threshold is determined. According to the hypothesis test theory, a constructed statistic can judge whether an object is moved or not. A statistic can be a difference d at the current location, a combination of all d inside a block or a template, then a determination on the current pixel, the central pixel, or a block is made. Experiments on different statistics and judging objects show that the test methods based on the two statistical distributions can make good results on test videos to different extent; but the test based on Laplace distribution is better in terms of overall performance. Experiments also show the proposed "determination on pixels—median filtering—macro-blocks mask" strategy is more robust.
3. Study of object-based video compression for enhanced surveillance region
In the proposed object-based coding framework for surveillance application, the background without surveillance information is coded once then sent to the receiver (with update online). Surveillance regions are enhanced at first to achieve the required visual effect. The shape information of surveillance objects is represented as macro-block coordinates. The temporal redundancy is eliminated by motion estimation within the surveillance region in the reference frame. Since the number of luminance levels is reduced due to enhancement process, the residual error of motion estimation is compressed with lossless entropy coding. Experimental results show that the proposed scheme can produce surveillance region with better contrast and significant compression efficiency at the same time.
In order to adapt to the proposed object-based coding for surveillance video, an adaptive and fast motion estimation algorithm integrated with multiple advantages is proposed. These advantages include the prediction for search start points, different search patterns, half-way stop technology, and a search rule with priority. Experiments show that the proposed algorithm presents a competitive performance with low computation cost.
4. Study of region of interest (ROI) based surveillance video compression
For a kind of surveillance video produced by fixed camera and fixed surveillance region, the surveillance region is defined as ROI. The video is divided into group of frames (GOF). 3D wavelet transform is performed on each GOR The ROI of every frame is defined as a rectangular with fixed location specified by the coordinates of left top and right bottom corners. The samples between the two corners belong to ROI mask. Wavelet coefficients inside 3D ROI is scaled up. Then the GOF is coded by 3D SPIHT algorithm and thus ROI is assigned bit overhead with priority. Experiments show that surveillance regions can be reconstructed with priority and achieve better visual quality.
一、旨在提供报警信号和启动压缩程序的运动检测研究
因为相邻两帧存在运动会引起相同位置上DCT系数大小和符号的改变，所以通过比较DCT系数的符号就可以确定是否有运动对象出现。而M-JPEG的码流特点表明在压缩码流中可以获得每帧的DCT系数的符号。为了消除随机噪声带来的影响，将其引起DCT系数块符号变化数目模型化为高斯分布，以此确定运动块阈值。场景照度突然变化带来的大量虚假运动块数用中值滤波加以消除。最后以校准帧最大的运动块数作为确定当前帧是否存在运动的阈值。实验结果表明，在不同的量化矩阵、不同的运动块阈值下，均能实现正确的检测。
二、以运动物体作为监控对象的分割研究
将噪声分布近似为Gauss分布或Laplace分布，再设定误判概率进而确定一个阈值就可以利用假设检验的思想，构成统计量对不同对象判断其运动与否。以当前位置灰度差值d、模板内、以及分块内所有d的不同组合作为检验统计量，对当前位置像素、模板中心像素、分块进行判决。针对不同统计量和判决对象的实验结果表明，基于这两种分布的检验方法都能不同程度地对测试视频分割出较好的结果，但基于Laplace的检验方法的综合性能较优。实验结果还表明本文提出的“像素判决一中值滤波一宏块掩模”策略具有较好的健壮性。
三、基于增强区域的监控视频压缩研究
在本文提出的面向监控应用的基于对象的视频编码框架中，不含监控信息的背景只编码一次发送到接收端(融合在线更新机制)。对于监控对象区域，首先对其进行增强以达到所需的视觉效果。监控对象的形状信息表示为宏块坐标；监控对象之间的时间冗余通过运动估计来消除，但搜索范围限制在前一帧监控对象区域之内；由于增强处理使得监控区域的灰度级减少了，所以对运动估计的残差进行无损熵编码。实验结果表明，本文方案得到的重建视频帧能提供对比度增强的监控对象区域，同时又能达到可观的压缩效率。
为了适应本文的基于对象的监控视频编码，本文还提出了一种综合多种优点的自适应快速运动估计算法。包括搜索起点的预测、不同的搜索模式、中途停止技术、以及优先搜索机制。实验结果显示，本文的运动估计算法既能提供与其他算法可比的性能，又有很低的计算复杂度。
四、基于感兴趣区域(ROI：Region OfInterest)编码思想的监控视频压缩研究
针对摄像机固定监控区域固定的一类监控视频，本文以监控对象区域为ROI。首先将视频分为固定大小的帧组(GOF：Group OfFrames)，然后对GOF进行3D小波变换。每一帧内ROI设为矩形而且位置相同，由左上角和右下角坐标描述，在这两个角之间所有的样值都属于ROI掩模。3D ROI内的小波系数先被上移，然后使用3D SPIHT算法进行编码，使ROI得到比特资源的优先分配。实验表明，在不同的比特率下，监控区域总能得到优先的重建，获得较好的重建质量。
By analyzing information from one or several cameras, a visual surveillance system can supervise and control fields distributed in large and complex areas. The present main research concentrate on how to realize or substitute human vision, or achieve a surveillance system with automation as much as possible, while study on compression of surveillance video is fewer relatively. In fact, the efficient compression of surveillance video with huge data volume is needed yet. Furthermore, new requirement is proposed on surveillance video coding. In addition, video compression inside a surveillance system is not a single problem; instead, it is a system problem with video pre-processing. Therefore, it is important to study compression of surveillance video. This paper includes three cues: one, motion detection in order to provide an remote alarm signal for supervisors or an indication for the start of local compression procedure; the other, motion segmentation for acquisition of surveillance objects and thereafter object-based video coding; the third, ROI video coding in view of some special surveillance cases. The main ideas of this paper are outlined as follows.
1. Study of motion detection to provide an alarm signal and a start indication of video compression
Because motion between adjacent frames can cause change in terms of DCT coefficient amplitudes and signs, the appearance of motion objects can be determined by the change of signs of DCT coefficients. It is showed that signs of DCT coefficients can be acquired from the M-JPEG code stream. In order to eliminate the impact imposed by random noise, the number of DCT coefficient sign changes caused by the noise is modeled as Gauss distribution, which can determine a threshold of motion blocks. The large number of false motion blocks brought by abrupt change of scene luminance is removed by median filtering. At last, the biggest number of motion blocks of a frame in the calibration stage is used as a threshold of motion frames. Experiments show accurate motion detection can be achieved under different quantization matrixes and/or different motion blocks thresholds.
2. Study of segmentation of surveillance objects with motion
The camera noise statistical characteristic is approximated as Gauss or Laplace distribution, then an error probability is set and a threshold is determined. According to the hypothesis test theory, a constructed statistic can judge whether an object is moved or not. A statistic can be a difference d at the current location, a combination of all d inside a block or a template, then a determination on the current pixel, the central pixel, or a block is made. Experiments on different statistics and judging objects show that the test methods based on the two statistical distributions can make good results on test videos to different extent; but the test based on Laplace distribution is better in terms of overall performance. Experiments also show the proposed "determination on pixels—median filtering—macro-blocks mask" strategy is more robust.
3. Study of object-based video compression for enhanced surveillance region
In the proposed object-based coding framework for surveillance application, the background without surveillance information is coded once then sent to the receiver (with update online). Surveillance regions are enhanced at first to achieve the required visual effect. The shape information of surveillance objects is represented as macro-block coordinates. The temporal redundancy is eliminated by motion estimation within the surveillance region in the reference frame. Since the number of luminance levels is reduced due to enhancement process, the residual error of motion estimation is compressed with lossless entropy coding. Experimental results show that the proposed scheme can produce surveillance region with better contrast and significant compression efficiency at the same time.
In order to adapt to the proposed object-based coding for surveillance video, an adaptive and fast motion estimation algorithm integrated with multiple advantages is proposed. These advantages include the prediction for search start points, different search patterns, half-way stop technology, and a search rule with priority. Experiments show that the proposed algorithm presents a competitive performance with low computation cost.
4. Study of region of interest (ROI) based surveillance video compression
For a kind of surveillance video produced by fixed camera and fixed surveillance region, the surveillance region is defined as ROI. The video is divided into group of frames (GOF). 3D wavelet transform is performed on each GOR The ROI of every frame is defined as a rectangular with fixed location specified by the coordinates of left top and right bottom corners. The samples between the two corners belong to ROI mask. Wavelet coefficients inside 3D ROI is scaled up. Then the GOF is coded by 3D SPIHT algorithm and thus ROI is assigned bit overhead with priority. Experiments show that surveillance regions can be reconstructed with priority and achieve better visual quality.
引文
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