视音频编解码技术发展现状和展望
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资料包括： 论文(14页14269字)
说明：
1 视频编解码技术发展现状
2 音频编解码技术发展现状
3 视频编解码技术与安防应用
4 视音频编解码技术展望
5 参考文献
参考文献：
1.ThomasWiegand, G.J.S., Senior Member, IEEE, Gisle Bjøntegaard, and Ajay Luthra, Senior Member, IEEE, Overview of the H.264/AVC Video Coding Standard. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2003. 13(7): p. 17.
2.周秉锋, 郑.叶., JVT草案中的核心技术综述. 软件学报, 2004. 15(1): p. 11.
3.Ostermann, J., Hybrid Coding: Where Can Future Gains Come from?” 2005.
4.Julien Reichel, H.S., Mathias Wien, Scalable Video Coding – Working Draft 2, JVT, Editor. 2005.
5.Dr Francesco Ziliani, J.-C.M., Scalable Video Coding In Digital Video Security. 2005. p. 19.
6.Wallace Kai-Hong Ho; Wai-Kong Cheuk; Lun, D.P.-K., Content-based scalable H.263 video coding for road traffic monitoring. IEEE Transactions on Multimedia, 2005. 7(4): p. 9.
7.Ser-Nam Lim; Davis, L.S.E., A., Scalable image-based multi-camera visual surveillance system, in AVSS.2003. 2003.
8.Nicolas, H., Scalable video compression scheme for tele-surveillance applications based on cast shadow detection and modelling, in Image Processing, 2005. ICIP 2005. IEEE International Conference on. 2005.
9.May, A.T., J.; Hobson, P.; Ziliani, F.; Reichel, J.;, Scalable video requirements for surveillance applications. Intelligent Distributed Surveilliance Systems, IEE, 2004: p. 4.
10.陶钧, 王., 张军, 姜志宏, 三维小波视频编码的可伸缩性研究. 小型微型计算机系统, 2005. 26(2).
11.ping., L.Y., A true th ree2dimension wavelet transfo rm
technique and its app lication, in Video Image Coding-Electronic Engineering. 2002. p. 52-59.
12.Wen-Hsiao Peng, C.-Y.T., Tihao Chiang, and Hsueh-Ming Hang, Advances of MPEG Scalable Video Coding Standard. 2005.
13.Robert T. Collins, A.J.L., Takeo Kanade,, et al., A System for Video Surveillance and Monitoring. 2000, The Robotics Institute, Carnegie Mellon University, Pittsburgh PA. p. 69.
14.Ismail Haritaoglu, M., IEEE, David Harwood, Member, IEEE, and Larry S. Davis, Fellow, IEEE, W4:Real-Time Surveillance of People and Their Activities. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
15.AVC, I.-T.R.I.I.-. Advanced Video Coding for Generic Audiovisual Service. 2005.
16.Robert Pless, T.B.y., and Yiannis Aloimonos, Detecting Independent Motion: The Statistics of Temporal Continuity. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8): p. 6.
17.L. Wixson, M., IEEE Computer Society, Detecting Salient Motion by Accumulating
Directionally-Consistent Flow. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
18.Medioni, I.C.G., Detecting and Tracking Moving Objects for Video Surveillance. 1999.
19.Tarak Gandhi, M.M.T., et al., Motion Analysis of OmniDirectional Video Streams for a Mobile Sentry. 2003.
20.Kakadiaris, C.B.o.a.I.A., et al., A Convex Penalty Method For Optical Human Motion Tracking. 2003.
21.Zhang, Z.M., et al., Independent Motion Detection Directly from Compressed Surveillance Video. 2003.
22.G., W.G.a.B. An introduction to the Kalman filter. 2000 [cited; Available from: http://www.cs.unc.edu.
23.A., I.M.a.B., Condensation—conditional density propagation for visual tracking. International Journal of Computer Vision, 1998. 25(1): p. 24.
24.Pavlović V, R.J., Cham T-J and Murphy K., A dynamic Bayesian network approach to figure tracking using learned dynamic models, in IEEE International Conference on Computer Vision,. 1999: Corfu, Greece,.
25.Chris Stauffer, M., IEEE, and and M. W. Eric L. Grimson, IEEE, Learning Patterns of Activity Using Real-Time Tracking. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
26.Bouthemy, Y.R.a.P., Real-Time Tracking of Moving Persons by Exploiting Spatio-Temporal Image Slices. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
27.Jinman Kang, I.C., Gérard Medioni, U.C.V.G. IRIS, and U.o.S. California, Multi-Views Tracking Within and Across Uncalibrated Camera Streams. 2003.
28.Hang-Bong Kang, S.-H.C., D.o.c. Eng., and T.C.U.o. Korea, Adaptive Object Tracking using Bayesian Network and Memory. 2004.
29.Bruno Müller Junior, R.d.O.A., I.d. Computaçao, and U.E.d. Campinas, Distributed RealTime Soccer Tracking. 2004.
30.R. Cucchiara, C.G., G. Tardini and D.d.I.I.-U.o.M.a.R. Emilia, Track-based and Object-based Occlusion for People Tracking Refinement in Indoor Surveillance. 2004.
31.Cash J. Costello, C.P.D., Amit Banerjee, Hesky Fisher, A.P. Laboratory, and J.H. University, Scheduling an Active Camera to Observe People. 2004.
32.Marco Cristani, M.B., Vittorio Murino, U.o. Verona, and D.d. Informatica, Multilevel Background Initialization using Hidden Markov Models. 2003.
33.Collins, R.T.L., A.J.; Fujiyoshi, H.; Kanade, T., Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE, 2001. 89(10): p. 12.
34.Utsumi A, M.H., Ohya J and Yachida M., Multiple-view-based tracking of multiple humans., in IEEE International Conference on Pattern Recognition. 1998: Brisbane, Australia,.
35.J., C.Q.a.A., Tracking human motion using multiple cameras., in International Conference on Pattern Recognition. 1996: Vienna.
36.Marchesotti, L.M., L.; Regazzoni, C.;, A video surveillance architecture for alarm generation and video sequences retrieval, in 2002 International Conference on Image Processing (ICIP2002). 2002.
37.Sangho Park, J.K.A., et al., Recognition of Two-person Interactions Using a Hierarchical Bayesian Network. 2003.
38.Spagnolo, M.L.T.D.O.P., I.o.I.S. for, and Automation, Human Activity Recognition for Automatic VisualSurveillance of Wide Areas. 2004.
作者点评：
由于数字视频编码的核心是对信号进行压缩，所以不断提高编码压缩效率仍是混合编码的主要发展目标。但是追求更高的压缩效率需要对传统的“变换+运动补偿+基于视觉的量化+熵编码”框架有所突破，给视频编码性能带来新的提升。
可伸缩的视频编码技术因为具有良好的网络适应性，所以围绕它的应用，尤其是网络环境下的应用，会越来越多。可以预见，在未来的网络视频监控中，可伸缩技术将是保证网络传输质量的一个重要实现技术。
而多视点编码方法的研究会集中在多视点视频的采集与校准，场景深度及几何信息获取（立体匹配），多视点视频编码，多视点视频通信，新视图渲染以及最终的交互或立体显示等6大关键上，这些技术的突破会为自由视点电视（FTV）、立体电视（3DTV）和沉浸感视频会议的应用提供技术支持。
作为SVC、MVC等各类视频编码的基础，混合框架的编码仍有很强的生命力。同时随着网络、通信、娱乐业对数字媒体的广泛需求，AVS、H.264这一代标准被普遍接受，相应的产品开发工作相当重要。包括编解码芯片、整机和系统。应用领域涉及数字电视、卫星电视、移动电视、手机电视、网络电视、时移电视机、新一代光盘存储媒体、安防监控、智能交通、会议电视、可视电话、数字摄像机等等。其中，安防监控领域是音视频编解码技术的主要应用领域之一。编解码技术在这个领域的应用，需要结合安防监控领域的特殊需求进行研究。只有在这个方向掌握有自主知识产权的核心技术，我国的安防监控产业才能健康持续的发展。
资料包括： 论文(14页14269字)
说明：
摘 要： 随着国内外市场对多媒体应用需求的不断增加，人们对视频、音频服务质量的期望不断提升，视音频编解码技术越来越多的受到重视，并在近十几年里得到了飞速的发展。本文在分别介绍了视音频编解码技术的主要内容和国内外发展现状之后，对视频编解码技术在安防中的应用情况进行分析，并且在最后对视音频编解码技术的发展趋势进行了展望。
关键词：视频编解码技术；音频编解码技术
引言
近年来，市场对于视频会议、视频监控、交互电视、智能语音识别等多媒体应用的需求不断增加，并且用户对于多媒体服务质量的期望也越来越高。为了有效防止信号在传输和存储过程中引入噪声和导致波形畸变，获得更好的品质，模拟信号（音频信号、图像和视频信号）一般需要经过采样和数字化，然后再进行存储、传输和重建。但这些数字化信号的信息量极大，尽管海量存储技术、处理器的速度和数字通信系统的性能有了迅猛发展，但对数据的存储能力和数据传输带宽的需求仍然超出了现有技术的能力所及。为了使通信成为可能，并尽可能的降低通信的费用，优秀的信源编解码性能是必需的。因此，视音频编解码技术作为信源编码技术，是多媒体应用技术的基础和核心。本文的第一章和第二章就国内外视音频编解码技术的发展现状进行了详细阐述，继而对国内外的研究现状进行了对比分析。鉴于视频编解码技术在安防应用中属于核心技术之一，本文在第三章分析了这两者之间的关系。最后，第四章对视音频编解码技术的发展趋势进行了展望。
1视频编解码技术发展现状
视频编码的主要目的就是在保证一定重构质量的前提下，以尽量少的比特数来表征视频信息。视频编码的原理是：由于表示图像和视频信息所需的大量的数据往往是高度相关的，这些相关性会引起信息的冗余，因此可以通过去除这些冗余信息来实现对视频数据的压缩。传统的基于统计特性的运动补偿＋变换编码的混合编码框架在数据压缩方面取得了很大的成果，国内外的通用视频压缩标准均基于此框架，比如H.26L系列、MPEG系列以及我国的AVS－P2标准。但是随着计算机网络的不断发展和应用需求的多样化，对于视频编码技术的研究不再仅仅局限于压缩特性，而渐渐开始向网络适应性、用户交互性等方面转移。因此，这几年来，视频编码技术一方面继续以混合编码为框架研究如何进一步提高压缩特性，另一方面不断的向可伸缩编码、多视点编码等分支方向发展。
1 视频编解码技术发展现状
2 音频编解码技术发展现状
3 视频编解码技术与安防应用
4 视音频编解码技术展望
5 参考文献
参考文献：
1.ThomasWiegand, G.J.S., Senior Member, IEEE, Gisle Bjøntegaard, and Ajay Luthra, Senior Member, IEEE, Overview of the H.264/AVC Video Coding Standard. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2003. 13(7): p. 17.
2.周秉锋, 郑.叶., JVT草案中的核心技术综述. 软件学报, 2004. 15(1): p. 11.
3.Ostermann, J., Hybrid Coding: Where Can Future Gains Come from?” 2005.
4.Julien Reichel, H.S., Mathias Wien, Scalable Video Coding – Working Draft 2, JVT, Editor. 2005.
5.Dr Francesco Ziliani, J.-C.M., Scalable Video Coding In Digital Video Security. 2005. p. 19.
6.Wallace Kai-Hong Ho; Wai-Kong Cheuk; Lun, D.P.-K., Content-based scalable H.263 video coding for road traffic monitoring. IEEE Transactions on Multimedia, 2005. 7(4): p. 9.
7.Ser-Nam Lim; Davis, L.S.E., A., Scalable image-based multi-camera visual surveillance system, in AVSS.2003. 2003.
8.Nicolas, H., Scalable video compression scheme for tele-surveillance applications based on cast shadow detection and modelling, in Image Processing, 2005. ICIP 2005. IEEE International Conference on. 2005.
9.May, A.T., J.; Hobson, P.; Ziliani, F.; Reichel, J.;, Scalable video requirements for surveillance applications. Intelligent Distributed Surveilliance Systems, IEE, 2004: p. 4.
10.陶钧, 王., 张军, 姜志宏, 三维小波视频编码的可伸缩性研究. 小型微型计算机系统, 2005. 26(2).
11.ping., L.Y., A true th ree2dimension wavelet transfo rm
technique and its app lication, in Video Image Coding-Electronic Engineering. 2002. p. 52-59.
12.Wen-Hsiao Peng, C.-Y.T., Tihao Chiang, and Hsueh-Ming Hang, Advances of MPEG Scalable Video Coding Standard. 2005.
13.Robert T. Collins, A.J.L., Takeo Kanade,, et al., A System for Video Surveillance and Monitoring. 2000, The Robotics Institute, Carnegie Mellon University, Pittsburgh PA. p. 69.
14.Ismail Haritaoglu, M., IEEE, David Harwood, Member, IEEE, and Larry S. Davis, Fellow, IEEE, W4:Real-Time Surveillance of People and Their Activities. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
15.AVC, I.-T.R.I.I.-. Advanced Video Coding for Generic Audiovisual Service. 2005.
16.Robert Pless, T.B.y., and Yiannis Aloimonos, Detecting Independent Motion: The Statistics of Temporal Continuity. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8): p. 6.
17.L. Wixson, M., IEEE Computer Society, Detecting Salient Motion by Accumulating
Directionally-Consistent Flow. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
18.Medioni, I.C.G., Detecting and Tracking Moving Objects for Video Surveillance. 1999.
19.Tarak Gandhi, M.M.T., et al., Motion Analysis of OmniDirectional Video Streams for a Mobile Sentry. 2003.
20.Kakadiaris, C.B.o.a.I.A., et al., A Convex Penalty Method For Optical Human Motion Tracking. 2003.
21.Zhang, Z.M., et al., Independent Motion Detection Directly from Compressed Surveillance Video. 2003.
22.G., W.G.a.B. An introduction to the Kalman filter. 2000 [cited; Available from: http://www.cs.unc.edu.
23.A., I.M.a.B., Condensation—conditional density propagation for visual tracking. International Journal of Computer Vision, 1998. 25(1): p. 24.
24.Pavlović V, R.J., Cham T-J and Murphy K., A dynamic Bayesian network approach to figure tracking using learned dynamic models, in IEEE International Conference on Computer Vision,. 1999: Corfu, Greece,.
25.Chris Stauffer, M., IEEE, and and M. W. Eric L. Grimson, IEEE, Learning Patterns of Activity Using Real-Time Tracking. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
26.Bouthemy, Y.R.a.P., Real-Time Tracking of Moving Persons by Exploiting Spatio-Temporal Image Slices. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000. 22(8).
27.Jinman Kang, I.C., Gérard Medioni, U.C.V.G. IRIS, and U.o.S. California, Multi-Views Tracking Within and Across Uncalibrated Camera Streams. 2003.
28.Hang-Bong Kang, S.-H.C., D.o.c. Eng., and T.C.U.o. Korea, Adaptive Object Tracking using Bayesian Network and Memory. 2004.
29.Bruno Müller Junior, R.d.O.A., I.d. Computaçao, and U.E.d. Campinas, Distributed RealTime Soccer Tracking. 2004.
30.R. Cucchiara, C.G., G. Tardini and D.d.I.I.-U.o.M.a.R. Emilia, Track-based and Object-based Occlusion for People Tracking Refinement in Indoor Surveillance. 2004.
31.Cash J. Costello, C.P.D., Amit Banerjee, Hesky Fisher, A.P. Laboratory, and J.H. University, Scheduling an Active Camera to Observe People. 2004.
32.Marco Cristani, M.B., Vittorio Murino, U.o. Verona, and D.d. Informatica, Multilevel Background Initialization using Hidden Markov Models. 2003.
33.Collins, R.T.L., A.J.; Fujiyoshi, H.; Kanade, T., Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE, 2001. 89(10): p. 12.
34.Utsumi A, M.H., Ohya J and Yachida M., Multiple-view-based tracking of multiple humans., in IEEE International Conference on Pattern Recognition. 1998: Brisbane, Australia,.
35.J., C.Q.a.A., Tracking human motion using multiple cameras., in International Conference on Pattern Recognition. 1996: Vienna.
36.Marchesotti, L.M., L.; Regazzoni, C.;, A video surveillance architecture for alarm generation and video sequences retrieval, in 2002 International Conference on Image Processing (ICIP2002). 2002.
37.Sangho Park, J.K.A., et al., Recognition of Two-person Interactions Using a Hierarchical Bayesian Network. 2003.
38.Spagnolo, M.L.T.D.O.P., I.o.I.S. for, and Automation, Human Activity Recognition for Automatic VisualSurveillance of Wide Areas. 2004.
作者点评：
由于数字视频编码的核心是对信号进行压缩，所以不断提高编码压缩效率仍是混合编码的主要发展目标。但是追求更高的压缩效率需要对传统的“变换+运动补偿+基于视觉的量化+熵编码”框架有所突破，给视频编码性能带来新的提升。
可伸缩的视频编码技术因为具有良好的网络适应性，所以围绕它的应用，尤其是网络环境下的应用，会越来越多。可以预见，在未来的网络视频监控中，可伸缩技术将是保证网络传输质量的一个重要实现技术。
而多视点编码方法的研究会集中在多视点视频的采集与校准，场景深度及几何信息获取（立体匹配），多视点视频编码，多视点视频通信，新视图渲染以及最终的交互或立体显示等6大关键上，这些技术的突破会为自由视点电视（FTV）、立体电视（3DTV）和沉浸感视频会议的应用提供技术支持。
作为SVC、MVC等各类视频编码的基础，混合框架的编码仍有很强的生命力。同时随着网络、通信、娱乐业对数字媒体的广泛需求，AVS、H.264这一代标准被普遍接受，相应的产品开发工作相当重要。包括编解码芯片、整机和系统。应用领域涉及数字电视、卫星电视、移动电视、手机电视、网络电视、时移电视机、新一代光盘存储媒体、安防监控、智能交通、会议电视、可视电话、数字摄像机等等。其中，安防监控领域是音视频编解码技术的主要应用领域之一。编解码技术在这个领域的应用，需要结合安防监控领域的特殊需求进行研究。只有在这个方向掌握有自主知识产权的核心技术，我国的安防监控产业才能健康持续的发展。