交通监控可伸缩视频编码研究
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摘要
可伸缩视频编码(Scalable Video Coding, SVC:本文主要指H.264/SVC)是指通过一次高压缩率编码,可以形成在时间上、空间上、质量上分层的多层码流,并可以根据应用场景、用户需求、网络环境和终端用户特征抽取所需要层的码流。SVC这种只提供唯一可伸缩的视频码流来支持多种设备和异构网络的技术优势非常适合视频监控系统的应用,为监控视频的压缩、存储和传输提供了强有力的技术支持。在视频监控领域中,交通监控是获取各种交通信息来解决交通问题的主要手段,人们的研究更多的集中在交通对象(主要是汽车)的分割、分析、识别与跟踪,交通特征参数的提取与分析,交通事件的识别与理解等领域,而针对交通监控视频进行压缩编码的研究相对较少。事实上,对于海量级别的交通监控视频更需要快速和高效的压缩编码技术,同时在编码阶段就要考虑压缩域与非压缩域视频检索等问题,因此,交通监控系统内的视频压缩不是一个普通的孤立的视频压缩问题,而是一个必须考虑预处理并且与视频/图像分析模块进行交互的系统问题,该问题已经成为视频压缩编码技术中的一个新兴研究领域,对于交通安防等领域有着巨大的应用价值。
鉴于此,本文研究面向交通监控应用的可伸缩视频编码,主要工作及研究成果在于：
1)提出适用于交通监控视频的时间可伸缩帧间快速编码算法
可伸缩视频编码采用分级B帧来实现时间伸缩性,其计算复杂度大大增加了编码时间,当前先进的改进算法可以将编码时间节省45%左右,但并未针对交通监控视频特点做进一步优化。对此,本文提出一种基于背景差分法的适合分级B帧的快速算法。首先通过改进的基于单高斯模型的时空背景建模方法获取背景图像；再求当前图像与背景图像的差值图像,通过滑动窗口去抖动算法正确获取运动区域；然后结合监控视频编码中影响分级B帧宏块模式选择的关键参数的统计数据分析,来缩减可能编码模式,从而提高编码速度。实验表明,本算法与标准算法比较,在保证编码效率和解码视频质量的前提下,编码时间可以平均节省约85%。
2)提出基于视觉注意模型的道路监控视频关键帧提取算法
对于日益剧增的海量交通监控视频,关键帧提取是和视频检索、摘要、浏览以及压缩编码都相关的一项重要技术。对此,本文首次提出一种基于视觉注意模型的道路监控视频关键帧提取算法。首先通过运动检测获取运动目标,以车牌和车辆最佳清晰度位置作为注意度评价标准,并提取运动目标位置显著度；然后提取运动目标的运动方向和强度显著度；接着用一种简单有效的车辆位置优先的自适应线性混合模式合成每帧图像的视觉注意度并生成最终的视觉注意度曲线；最后求出视觉注意度曲线的导数曲线,自适应滤波处理后在正值到负值变化的零交叉点中选取显著度最高的图像作为关键帧。实验证明,该算法提取的关键帧不但包括了所有经过监控的车辆最佳或接近最佳清晰度的位置,而且还能包括道路停车、超速和逆向行驶等各种交通事件,符合交通观察者的视觉特性。
3)提出跟踪感知下适于快速检索的均衡自适应GOP编码算法在关键帧获取的基础上,形成时间0级上的Ⅰ帧,对该类型Ⅰ帧中的交通事件和交通对象进行H.264/SVC监控信息扩展编码,并利用统一的语法和语义实现检索接口的标准化和统一化,从而极大的提高了压缩域内的检索速度和后期分析需要。但是这会引起Ⅰ帧位置的不定性,对分级B帧结构也造成一定程度的破坏,影响了帧间相关性和分级特性。对此,本文提出一种均衡自适应的GOP结构,并使用一种二叉树算法来实现任意大小GOP的时间分级特性。这样有利于检索和视频摘要的生成,但是由于部分Ⅰ帧的插入和监控信息扩展编码,率失真性能会有一定的损失,考虑到交通视频区别于一般的电视广播视频,其主要目的是在交通对象准确跟踪的基础上进行高层语义分析。对此,本文使用跟踪准确度代替PSNR来进行解码视频质量的度量,并通过优化变换系数的量化来去除更多对跟踪影响较小的码流。实验表明,在保证跟踪准确度相对准确的前提下,比传统方法可以减少约60%的码率。
4)提出适用于交通监控的内容自适应空间可伸缩视频编码算法
当前空间可伸缩编码系统很少考虑视频感兴趣区域(Region of Interest, ROI)或视觉突出内容,从而不能更好的适应视觉重要内容在较低分辨率移动终端的显示。对此,本文提出一种适用于交通监控的内容自适应空间可伸缩视频编码算法。首先使用背景差法获取运动车辆并进行目标跟踪,通常在交通监控中主导车辆(视觉上最突出的乍辆)所在运动窗为ROI,然后将该ROI设定为裁剪窗口(Cropping Window),使用H.264/SVC标准的扩展空间可伸缩方法(Extended Spatial Scalability, ESS)进行编码,并在空间增强层通过ROI分层量化策略和频域系数压缩矩阵的方法来进一步提高编码的率失真性能。实验表明,本算法与传统的二分下采样空间可伸缩算法相比,在率失真性能略有损失的情况下,对低分辨率空间层解码下的视觉感知有较大的改善。同样,在增强层仍可使用跟踪准确度代替SNR进行视频质量度量,在此标准下可以获得更高的编码性能提升。5)提出适于交通监控视频的容错编码与错误隐藏算法
3G技术的发展与4G/LTE技术的快速演进,使得异构环境下的各种移动与无线监控视频业务大大增加。由于移动无线信道的时变、高误码率、有限带宽等特点,其传输错误会严重影响SVC码流的解码质量。对此,本文提出空间可伸缩编码下基于反馈的错误跟踪(Error Tracking, ET)和参考帧选择(Reference Picture Selection, RPS)算法,结合了层间预测特性,并在选择帧内刷新(Intra Refresh)时考虑交通视频的视觉显著区域。并提出空间增强层帧级自适应错误隐藏算法,在对交通视频空间增强层宏块编码模式统计分析的基础上,利用基本层与帧间信息对增强层丢失宏块进行分析,自适应选择最佳的错误隐藏方法。如果使用跟踪准确度代替PSNR度量质量,由于压缩率大幅度提高,利用节省的码率增加冗余帧,在给定码率下,大大增加了解码视频的跟踪准确度。以上差错控制技术可以根据需求任意结合使用,为易错传输环境下接收视频的高层语义分析提供了有利的容错保障。
Scalable video coding (SVC, referring H.264/SVC in this paper) can form the temporal, spatial and SNR quality multilayer stream only by once high compression rate encoding, and the required layered bit steam can be extracted according to user needs, network environment and end-user features. This technical advantage is very suitable for the application of video surveillance system, and provides strong support for the compression, storage and transmission of the surveillance videos. In the field of video surveillance, traffic surveillance is the primary means to solve the traffic problems by extracting various traffic informations. More researches are focused on traffic objects segmentation, analysis, recognition and tracking, traffic characteristic parameters extraction and analysis, traffic events recognition and understanding, but the researches on the video compression coding of traffic surveillance are comparably few. In fact, the massive traffic surveillance videos need faster and more efficient compression encoding technology, and must consider the video retrieval etc. problems of compressed and non-compressed domain during encoding stage. Hence, the video compression of traffic surveillance is not an ordinary and isolated problem, but a system problem to pre-process and interact with the video/image analysis module. This issue has become an emerging research area, which has great applied values for traffic security etc. fields.
In view of this, the scalable video coding for traffic surveillance is studied in our paper, the main contents and novelties are as follows:
1) Fast inter prediction algorithm of temporal scalability for traffic surveillance video
Scalable video coding uses the hierarchical B frames to achieve temporal scalability, whose computational complexity significantly increases the coding time. The latest improved algorithm can save the coding time by about45%, but doesn't optimize for the traffic surveillance video features. In this paper a fast algorithm suitable for the hierarchical B frames based on the background difference method is proposed. First, the background image is gotten by the improved single Gaussian method based on spatio-temporal model. Second, the difference image is gotten by the current image subtracting the background image; the difference variances of each macro block and the sliding windows of the four directions are computed in order to remove the jitter effect, the motion region is acquire by comparing the minimal variance and the threshold. Then the analysis is processed by combining with the statistical data of the key parameters which will affect the macro block mode selection of the hierarchical B frames in the traffic surveillance to reduce the possible coding mode, so the coding speed is improved. The experimental results show that compared with the standard algorithm the proposed algorithm can save the coding time by about85%without degrading coding efficiency and decoding video quality.
2) Key frame extraction algorithm based on visual attention model
For the growing surge of massive traffic surveillance videos, key frame extraction is an important technology related to video retrieval, summary, browsing and compression. In this paper a key frame extraction algorithm based on visual attention model is proposed for lane surveillance video. First, the top-down method is used to detect moving objects whose position saliency is decided by the clearest position of license plates and vehicles. Then within the moving objects the bottom-up method is used to calculate the moving orientation and moving intensity saliency of the moving objects. Next the visual attention curve is fused by a simple adaptive linear mode. Last a derivative curve is generated, the frame with the most salient value in those zero-crossing points from the positive to the negative on derivative curve is selected as key frame. Experiments show that the key frames extracted by the proposed algorithm not only include the optimal or suboptimal positions of all passed vehicles, but also include on-street parking, speeding and reverse driving etc. traffic incidents. The results are consistent with the traffic observers'visual perception, and conducive to the extraction of vehicle static features to form the traffic video features database.
3) Tracking aware based proportionate GOP adaptation coding for fast retrieval
Base on the key frame extraction, the I-frames at the temporal0level are formed, in which the traffic events and traffic objects are encoded by the extended syntax and semantics as surveillance information of H.264/SVC. The unified defined syntax and semantic standardize the retrieval interface, which greatly improve the retrieval speed in the compressed domain and meet the post-analysis needs. But the key frames being encoded as I-frames will cause the uncertainty of the I-frame positions, thus the inter-frame correlation and temporal scalability of hierarchical B-picture are damaged in different degrees. In this paper the proportionate GOP adaptation structure is proposed, and the temporal scalability of any size GOP is realized by the proposed binary tree algorithm. This adaptive structure favors video retrieval and video summary generation, but there will be some loss of rate-distortion performance as part of the I-frame insertion. The traffic videos are different from the generic audiovisual services and broadcast television applications, its main aim is to analyze the high-level semantics based on the accurate traffic object tracking. Hence, the tracking accuracy instead of PSNR is utilized as the compression criterion, more low tracking interesting bit rate is reduced by optimizing the quantization of frequency coefficients. Experiments show that the method can save about60%bit rate compared with the conventional method while maintaining comparable tracking accuracy.
4) Content-adaptive traffic surveillance video coding with extended spatial scalability
Regions of interest (ROI) or visually salient regions are rarely considered in spatial scalable video coding, thus visually important content can not be better adapted to lower display resolutions. In this paper a content-adaptive spatial scalable coding for traffic surveillance video is proposed. First, the background image is extracted by an improved single Gaussian method. Then a background subtraction algorithm is present for detecting and tracking vehicles, the motion window of the leading vehicle is commonly referred to as ROI in traffic surveillance, and ROI is as cropping window in extended spatial scalability (ESS) of the scalable video coding (SVC). Moreover, we employ ROI-based quantization strategy and frequency coefficient suppression technique to improve the rate-distortion performance of enhancement spatial layer. The experimental results show that compared with the conventional scaling coding the proposed algorithm can greatly improve the visual perception of decoded base layer video with limited loss in rate-distortion performance. Also, the tracking accuracy instead of PSNR can be utilized as the compression criterion, by which the encoding performance can be improved more.
5) Error resilience and concealment algorithms for traffic surveillance video
By the development of3G and4G/LTE technology, the mobile and wireless video services increase significantly. Because of the time-varying, high bit error rate, limited bandwidth characteristics of mobile radio channels, the transmission errors will affect decoded quality of SVC bit stream. In this paper the feedback-based error tracking (ET) and reference picture selection (RPS) algorithms of the spatial scalability are proposed, which combine with the inter-layer prediction features. And in the choice of intra refresh, the visual salient regions of traffic videos are considered. Moreover, adaptive frame loss error concealment algorithm in spatial enhancement layer is proposed. Based on the statistical analysis of the enhancement layer macro block coding mode, the optimal error concealment method is adoptively chosen by using the basic layer and inter-frame information. If the tracking accuracy instead of PSNR is utilized as the compression criterion, the compression ratio is greatly improved. Part of the saving bit rate is utilized to introduce redundancy into the transmitted bit stream for error resilience. Under the given bit rate, the tracking accuracy of received video is greatly improved. The above error control techniques can be used in combination according to the requirements, which provide the conductive error resiliency for high-level semantic analysis of the transmitted bit stream in the error-prone channel.
鉴于此,本文研究面向交通监控应用的可伸缩视频编码,主要工作及研究成果在于：
1)提出适用于交通监控视频的时间可伸缩帧间快速编码算法
可伸缩视频编码采用分级B帧来实现时间伸缩性,其计算复杂度大大增加了编码时间,当前先进的改进算法可以将编码时间节省45%左右,但并未针对交通监控视频特点做进一步优化。对此,本文提出一种基于背景差分法的适合分级B帧的快速算法。首先通过改进的基于单高斯模型的时空背景建模方法获取背景图像；再求当前图像与背景图像的差值图像,通过滑动窗口去抖动算法正确获取运动区域；然后结合监控视频编码中影响分级B帧宏块模式选择的关键参数的统计数据分析,来缩减可能编码模式,从而提高编码速度。实验表明,本算法与标准算法比较,在保证编码效率和解码视频质量的前提下,编码时间可以平均节省约85%。
2)提出基于视觉注意模型的道路监控视频关键帧提取算法
对于日益剧增的海量交通监控视频,关键帧提取是和视频检索、摘要、浏览以及压缩编码都相关的一项重要技术。对此,本文首次提出一种基于视觉注意模型的道路监控视频关键帧提取算法。首先通过运动检测获取运动目标,以车牌和车辆最佳清晰度位置作为注意度评价标准,并提取运动目标位置显著度；然后提取运动目标的运动方向和强度显著度；接着用一种简单有效的车辆位置优先的自适应线性混合模式合成每帧图像的视觉注意度并生成最终的视觉注意度曲线；最后求出视觉注意度曲线的导数曲线,自适应滤波处理后在正值到负值变化的零交叉点中选取显著度最高的图像作为关键帧。实验证明,该算法提取的关键帧不但包括了所有经过监控的车辆最佳或接近最佳清晰度的位置,而且还能包括道路停车、超速和逆向行驶等各种交通事件,符合交通观察者的视觉特性。
3)提出跟踪感知下适于快速检索的均衡自适应GOP编码算法在关键帧获取的基础上,形成时间0级上的Ⅰ帧,对该类型Ⅰ帧中的交通事件和交通对象进行H.264/SVC监控信息扩展编码,并利用统一的语法和语义实现检索接口的标准化和统一化,从而极大的提高了压缩域内的检索速度和后期分析需要。但是这会引起Ⅰ帧位置的不定性,对分级B帧结构也造成一定程度的破坏,影响了帧间相关性和分级特性。对此,本文提出一种均衡自适应的GOP结构,并使用一种二叉树算法来实现任意大小GOP的时间分级特性。这样有利于检索和视频摘要的生成,但是由于部分Ⅰ帧的插入和监控信息扩展编码,率失真性能会有一定的损失,考虑到交通视频区别于一般的电视广播视频,其主要目的是在交通对象准确跟踪的基础上进行高层语义分析。对此,本文使用跟踪准确度代替PSNR来进行解码视频质量的度量,并通过优化变换系数的量化来去除更多对跟踪影响较小的码流。实验表明,在保证跟踪准确度相对准确的前提下,比传统方法可以减少约60%的码率。
4)提出适用于交通监控的内容自适应空间可伸缩视频编码算法
当前空间可伸缩编码系统很少考虑视频感兴趣区域(Region of Interest, ROI)或视觉突出内容,从而不能更好的适应视觉重要内容在较低分辨率移动终端的显示。对此,本文提出一种适用于交通监控的内容自适应空间可伸缩视频编码算法。首先使用背景差法获取运动车辆并进行目标跟踪,通常在交通监控中主导车辆(视觉上最突出的乍辆)所在运动窗为ROI,然后将该ROI设定为裁剪窗口(Cropping Window),使用H.264/SVC标准的扩展空间可伸缩方法(Extended Spatial Scalability, ESS)进行编码,并在空间增强层通过ROI分层量化策略和频域系数压缩矩阵的方法来进一步提高编码的率失真性能。实验表明,本算法与传统的二分下采样空间可伸缩算法相比,在率失真性能略有损失的情况下,对低分辨率空间层解码下的视觉感知有较大的改善。同样,在增强层仍可使用跟踪准确度代替SNR进行视频质量度量,在此标准下可以获得更高的编码性能提升。5)提出适于交通监控视频的容错编码与错误隐藏算法
3G技术的发展与4G/LTE技术的快速演进,使得异构环境下的各种移动与无线监控视频业务大大增加。由于移动无线信道的时变、高误码率、有限带宽等特点,其传输错误会严重影响SVC码流的解码质量。对此,本文提出空间可伸缩编码下基于反馈的错误跟踪(Error Tracking, ET)和参考帧选择(Reference Picture Selection, RPS)算法,结合了层间预测特性,并在选择帧内刷新(Intra Refresh)时考虑交通视频的视觉显著区域。并提出空间增强层帧级自适应错误隐藏算法,在对交通视频空间增强层宏块编码模式统计分析的基础上,利用基本层与帧间信息对增强层丢失宏块进行分析,自适应选择最佳的错误隐藏方法。如果使用跟踪准确度代替PSNR度量质量,由于压缩率大幅度提高,利用节省的码率增加冗余帧,在给定码率下,大大增加了解码视频的跟踪准确度。以上差错控制技术可以根据需求任意结合使用,为易错传输环境下接收视频的高层语义分析提供了有利的容错保障。
Scalable video coding (SVC, referring H.264/SVC in this paper) can form the temporal, spatial and SNR quality multilayer stream only by once high compression rate encoding, and the required layered bit steam can be extracted according to user needs, network environment and end-user features. This technical advantage is very suitable for the application of video surveillance system, and provides strong support for the compression, storage and transmission of the surveillance videos. In the field of video surveillance, traffic surveillance is the primary means to solve the traffic problems by extracting various traffic informations. More researches are focused on traffic objects segmentation, analysis, recognition and tracking, traffic characteristic parameters extraction and analysis, traffic events recognition and understanding, but the researches on the video compression coding of traffic surveillance are comparably few. In fact, the massive traffic surveillance videos need faster and more efficient compression encoding technology, and must consider the video retrieval etc. problems of compressed and non-compressed domain during encoding stage. Hence, the video compression of traffic surveillance is not an ordinary and isolated problem, but a system problem to pre-process and interact with the video/image analysis module. This issue has become an emerging research area, which has great applied values for traffic security etc. fields.
In view of this, the scalable video coding for traffic surveillance is studied in our paper, the main contents and novelties are as follows:
1) Fast inter prediction algorithm of temporal scalability for traffic surveillance video
Scalable video coding uses the hierarchical B frames to achieve temporal scalability, whose computational complexity significantly increases the coding time. The latest improved algorithm can save the coding time by about45%, but doesn't optimize for the traffic surveillance video features. In this paper a fast algorithm suitable for the hierarchical B frames based on the background difference method is proposed. First, the background image is gotten by the improved single Gaussian method based on spatio-temporal model. Second, the difference image is gotten by the current image subtracting the background image; the difference variances of each macro block and the sliding windows of the four directions are computed in order to remove the jitter effect, the motion region is acquire by comparing the minimal variance and the threshold. Then the analysis is processed by combining with the statistical data of the key parameters which will affect the macro block mode selection of the hierarchical B frames in the traffic surveillance to reduce the possible coding mode, so the coding speed is improved. The experimental results show that compared with the standard algorithm the proposed algorithm can save the coding time by about85%without degrading coding efficiency and decoding video quality.
2) Key frame extraction algorithm based on visual attention model
For the growing surge of massive traffic surveillance videos, key frame extraction is an important technology related to video retrieval, summary, browsing and compression. In this paper a key frame extraction algorithm based on visual attention model is proposed for lane surveillance video. First, the top-down method is used to detect moving objects whose position saliency is decided by the clearest position of license plates and vehicles. Then within the moving objects the bottom-up method is used to calculate the moving orientation and moving intensity saliency of the moving objects. Next the visual attention curve is fused by a simple adaptive linear mode. Last a derivative curve is generated, the frame with the most salient value in those zero-crossing points from the positive to the negative on derivative curve is selected as key frame. Experiments show that the key frames extracted by the proposed algorithm not only include the optimal or suboptimal positions of all passed vehicles, but also include on-street parking, speeding and reverse driving etc. traffic incidents. The results are consistent with the traffic observers'visual perception, and conducive to the extraction of vehicle static features to form the traffic video features database.
3) Tracking aware based proportionate GOP adaptation coding for fast retrieval
Base on the key frame extraction, the I-frames at the temporal0level are formed, in which the traffic events and traffic objects are encoded by the extended syntax and semantics as surveillance information of H.264/SVC. The unified defined syntax and semantic standardize the retrieval interface, which greatly improve the retrieval speed in the compressed domain and meet the post-analysis needs. But the key frames being encoded as I-frames will cause the uncertainty of the I-frame positions, thus the inter-frame correlation and temporal scalability of hierarchical B-picture are damaged in different degrees. In this paper the proportionate GOP adaptation structure is proposed, and the temporal scalability of any size GOP is realized by the proposed binary tree algorithm. This adaptive structure favors video retrieval and video summary generation, but there will be some loss of rate-distortion performance as part of the I-frame insertion. The traffic videos are different from the generic audiovisual services and broadcast television applications, its main aim is to analyze the high-level semantics based on the accurate traffic object tracking. Hence, the tracking accuracy instead of PSNR is utilized as the compression criterion, more low tracking interesting bit rate is reduced by optimizing the quantization of frequency coefficients. Experiments show that the method can save about60%bit rate compared with the conventional method while maintaining comparable tracking accuracy.
4) Content-adaptive traffic surveillance video coding with extended spatial scalability
Regions of interest (ROI) or visually salient regions are rarely considered in spatial scalable video coding, thus visually important content can not be better adapted to lower display resolutions. In this paper a content-adaptive spatial scalable coding for traffic surveillance video is proposed. First, the background image is extracted by an improved single Gaussian method. Then a background subtraction algorithm is present for detecting and tracking vehicles, the motion window of the leading vehicle is commonly referred to as ROI in traffic surveillance, and ROI is as cropping window in extended spatial scalability (ESS) of the scalable video coding (SVC). Moreover, we employ ROI-based quantization strategy and frequency coefficient suppression technique to improve the rate-distortion performance of enhancement spatial layer. The experimental results show that compared with the conventional scaling coding the proposed algorithm can greatly improve the visual perception of decoded base layer video with limited loss in rate-distortion performance. Also, the tracking accuracy instead of PSNR can be utilized as the compression criterion, by which the encoding performance can be improved more.
5) Error resilience and concealment algorithms for traffic surveillance video
By the development of3G and4G/LTE technology, the mobile and wireless video services increase significantly. Because of the time-varying, high bit error rate, limited bandwidth characteristics of mobile radio channels, the transmission errors will affect decoded quality of SVC bit stream. In this paper the feedback-based error tracking (ET) and reference picture selection (RPS) algorithms of the spatial scalability are proposed, which combine with the inter-layer prediction features. And in the choice of intra refresh, the visual salient regions of traffic videos are considered. Moreover, adaptive frame loss error concealment algorithm in spatial enhancement layer is proposed. Based on the statistical analysis of the enhancement layer macro block coding mode, the optimal error concealment method is adoptively chosen by using the basic layer and inter-frame information. If the tracking accuracy instead of PSNR is utilized as the compression criterion, the compression ratio is greatly improved. Part of the saving bit rate is utilized to introduce redundancy into the transmitted bit stream for error resilience. Under the given bit rate, the tracking accuracy of received video is greatly improved. The above error control techniques can be used in combination according to the requirements, which provide the conductive error resiliency for high-level semantic analysis of the transmitted bit stream in the error-prone channel.
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