面向监控的高效视频压缩技术
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摘要
随着视频处理与视频编码技术的发展,视频监控系统得到了广泛应用。人们对图像质量和压缩性能的要求是无止境的,高效的视频编码技术成为人们研究的热点。而现有的视频编码技术并没有充分考虑视频监控的特点,针对视频监控的视频编码还有较大的性能提升空间。
论文首先研究了监控视频的特点与需求,并分析了现有编码技术在监控应用中的不足,然后提出了针对性的视频编码方法,主要包含以下几个方面:
在监控视频中,常常存在大面积的静止区域,这部分区域不是监控的重点,现有编码标准对这些区域编码造成大量的码流浪费,为了更充分地压缩静止区域图像,同时又要保证运动区域图像的编码质量,论文提出了一种结合运动目标检测的编码算法,将图像分解成两个图层图像编码。一个是静止图层,可以拉长静止图层的图像组长度,采用图层跳过模式,有效地提高了静止区域的压缩率;另一个是运动图层,通过采用层间参考预测,提高了编码质量。实验表明这种编码方法是一种高效的视频编码方法。
在监控视频中,常常含有噪声,特别是光线不好的场景,监控视频中的噪声将变得很大,对这种视频的压缩率很低。针对这种情况,论文提出了一种视频滤波与自适应合成参考帧相结合的编码方法。视频滤波对噪声有很好的抑制效果,提高了视频编码的压缩率,但同时也会滤除图像纹理和运动细节,这在监控应用中是不利的;另一方面,合成参考帧是一种经过简单滤噪的参考帧图像,能提供更好的预测参考,有效地提高了编码图像的质量,论文提出了自适应合成参考帧的编码方法,实验证明自适应的合成参考帧能进一步地提高含噪视频的编码性能和图像质量,但这种编码方法没有对待编码图像进行滤波。论文充分考虑了视频滤波与合成参考帧的优缺点,将二者相结合,在保证图像细节和编码效率的前提下有效地抑制了图像噪声,同时大幅度提高了压缩效率。
Along with the development of the video process and video coding technology, video surveillance systems have been used widely. The requirement for the quality of the picture and the performance of compressing is endless, so that efficient video coding technology becomes the hot research topic. However, the existed video coding technology doesn't consider the characteristics of video surveillance enough. Therefore, there is more space to improve the performance of video coding for video surveillance.
Firstly, this paper studies the characteristics and the demands of surveillance video, and analyzes the disadvantage of existing coding techniques for surveillance, then proposes some video coding schemes, mainly includes the following aspects:
In surveillance applications, there are often large static areas, which are not the focus. It is a waste of bit stream to code these static areas. In order to compress these areas largely and improve the quality of the image in the motion areas, this paper proposes an algorithm combined with the motion detection technique, which divides the picture into static layer picture and motion layer picture. For the static layer picture, the algorithm adopts a larger GOP (group of the pictures), and adds a layer skip mode, which improve coding efficiency greatly. For the motion layer picture, the algorithm adopts inter-layer prediction to improve the quality of the picture. The experiment shows the algorithm is very efficient.
There is a lot of noise in surveillance video, especially in low light. It's difficult to improve coding efficiency for this kind of video. The paper proposes an algorithm, which combines video filter and adaptive synthesized reference (SR) picture. Video filter can achieve a better compression performance by reducing the noise. However, it will blur the texture details and the motion details at the same time. This is disadvantage in the surveillance applications. On the other hand, the SR picture is a new reference picture which is filtered simply. The SR picture can provide better prediction to improve the quality of the coding picture. Based on this, an adaptive SR picture is proposed. The experiment shows the adaptive SR picture can improve the performance of the video coding further. However this method just filters the reference picture but not the picture to be coded. The paper takes video filter and adaptive SR picture into consideration, combines both of them to suppress the video noise. At the same time, the proposed scheme improves the coding efficiency and preserves the picture details.
论文首先研究了监控视频的特点与需求,并分析了现有编码技术在监控应用中的不足,然后提出了针对性的视频编码方法,主要包含以下几个方面:
在监控视频中,常常存在大面积的静止区域,这部分区域不是监控的重点,现有编码标准对这些区域编码造成大量的码流浪费,为了更充分地压缩静止区域图像,同时又要保证运动区域图像的编码质量,论文提出了一种结合运动目标检测的编码算法,将图像分解成两个图层图像编码。一个是静止图层,可以拉长静止图层的图像组长度,采用图层跳过模式,有效地提高了静止区域的压缩率;另一个是运动图层,通过采用层间参考预测,提高了编码质量。实验表明这种编码方法是一种高效的视频编码方法。
在监控视频中,常常含有噪声,特别是光线不好的场景,监控视频中的噪声将变得很大,对这种视频的压缩率很低。针对这种情况,论文提出了一种视频滤波与自适应合成参考帧相结合的编码方法。视频滤波对噪声有很好的抑制效果,提高了视频编码的压缩率,但同时也会滤除图像纹理和运动细节,这在监控应用中是不利的;另一方面,合成参考帧是一种经过简单滤噪的参考帧图像,能提供更好的预测参考,有效地提高了编码图像的质量,论文提出了自适应合成参考帧的编码方法,实验证明自适应的合成参考帧能进一步地提高含噪视频的编码性能和图像质量,但这种编码方法没有对待编码图像进行滤波。论文充分考虑了视频滤波与合成参考帧的优缺点,将二者相结合,在保证图像细节和编码效率的前提下有效地抑制了图像噪声,同时大幅度提高了压缩效率。
Along with the development of the video process and video coding technology, video surveillance systems have been used widely. The requirement for the quality of the picture and the performance of compressing is endless, so that efficient video coding technology becomes the hot research topic. However, the existed video coding technology doesn't consider the characteristics of video surveillance enough. Therefore, there is more space to improve the performance of video coding for video surveillance.
Firstly, this paper studies the characteristics and the demands of surveillance video, and analyzes the disadvantage of existing coding techniques for surveillance, then proposes some video coding schemes, mainly includes the following aspects:
In surveillance applications, there are often large static areas, which are not the focus. It is a waste of bit stream to code these static areas. In order to compress these areas largely and improve the quality of the image in the motion areas, this paper proposes an algorithm combined with the motion detection technique, which divides the picture into static layer picture and motion layer picture. For the static layer picture, the algorithm adopts a larger GOP (group of the pictures), and adds a layer skip mode, which improve coding efficiency greatly. For the motion layer picture, the algorithm adopts inter-layer prediction to improve the quality of the picture. The experiment shows the algorithm is very efficient.
There is a lot of noise in surveillance video, especially in low light. It's difficult to improve coding efficiency for this kind of video. The paper proposes an algorithm, which combines video filter and adaptive synthesized reference (SR) picture. Video filter can achieve a better compression performance by reducing the noise. However, it will blur the texture details and the motion details at the same time. This is disadvantage in the surveillance applications. On the other hand, the SR picture is a new reference picture which is filtered simply. The SR picture can provide better prediction to improve the quality of the coding picture. Based on this, an adaptive SR picture is proposed. The experiment shows the adaptive SR picture can improve the performance of the video coding further. However this method just filters the reference picture but not the picture to be coded. The paper takes video filter and adaptive SR picture into consideration, combines both of them to suppress the video noise. At the same time, the proposed scheme improves the coding efficiency and preserves the picture details.
引文
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[3].ISO/IEC JTC1/SC29/WG11,ISO/IEC,MPEG-1 Committee Draft,CD11172:Information Technology.Dec.1991.
[4].ISO/IEC JTC1/SC29/WG11,ISO/IEC,MPEG-2 Committee Draft,CD 13818:Information Technology.Dec.1993.
[5].ISO/IEC JTC1/SC29/WG11,ISO/IEC,Coding of Audio-Visual Objects-Part2:Visual,ISO/IEC 14496-2(MPEG-2 Visual Version 1 ),Apt 1999.
[6].Massimo Ravasi,Marco Mattavelli,Christophe Clerc,A Computational Complexity Comparison of MPEG4 and JVT Codecs,Joint Video Team(JVT)of ISO/IEC MPEG & ITU-T VCEG(ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Q.6) 4th Meeting,Klagenfurt,Austria,22-26 July,2002.
[7].Joint Video Team(JVT) of ISO/IEC MPEG and ITU-T VCEG,Draft ITU-T recommendation and final draft international standard of joint video specification(ITU-T Rec.H.264/ISO/IEC 14496-10 AVC),JVT-G050,Pattaya,Mar.2003.
[8].Thomas Wiegand,Gary J.Sullivan,Gisle Bjntegaard,Ajay Luthra,Overview of the H.264/AVC video coding standard,IEEE Transactions On Circuits And Systems For Video Technology,VOL.13,NO.7,JULY 2003.
[9].J.Ostermann,J.Bormans,P.List,D.Marpe,M.Narroschke,F.Pereira,T.Stockhammer and T.Wedi,Video coding with H.264/AVC:tools,performance,and complexity,Circuits and Systems Magazine,IEEE,vol.4,pp.7-28,2004.
[10].中华人民共和国国家标准,信息技术:先进音视频编码,第二部分:视频(批稿)GB/TXXXXX.2-YYYY.
[11].虞露,AVS-视频技术概述,浙江大学,中国多媒体视讯,2004-03-08.
[12].李彦东,许生旺,AVS-P2和H.264标准的比较,专题技术与工程应用, Vo 1136 No 18,2006.
[13].Information technology - Advanced coding of audio and video - Part 2:Video AVS-S CD 1.0.
[14].韩军,邵志一,宋海华,在低码率信道中提高视频编码质量的方法,上海大学学报(自然科学版)第13卷第6期,2007年12月.
[15].King N.Ngan,Senior Member,IEEE,and Weng L.Chooi,Student Member,IEEE,Very Low Bit Rate Video Coding Using 3D Subband Approach,IEEE Transactions On Circuits And Systems For Video Technology,Vol.4,No.3,June 1994.
[16].A.Eleftheriadis,A.Jacquin,Low bit rate model-assisted H.261-compatible coding of video,icip,vol.2,pp.2418,1995 International Conference on Image Processing(ICIP'95) - Volume 2,1995
[17].Ralph Neff and Avideh Zakhor,Very Low Bit-Rate Video Coding Based on Matching Pursuits,IEEE Transactions on circuits and systems for video technology,vol.7,no.1,February 1997.
[18].Joint Draft 11:Scalable Video Coding,Joint Video Team(JVT) of ISO/IEC MPEG & ITU-T VCEG,Doe.JVT-X201,Jul.2007.
[19].Mathias Wien,Member,IEEE,Heiko Schwarz,and Tobias Oelbaum,"Performance Analysis of SVC",IEEE Transactions on circuits and systems for video technology,vol.17,no.9,2007.
[20].http://www.kinham.com/product.asp?one classid=3&two classid=88&id=290.
[21].D.Hepper and H.Li,Analysis of uncovered background prediction for image sequence coding,in Conf.Rec.PCS,1987,pp.192-193.
[22].T.Wiegand,X.Zhang,and B.Girod,Motion compensating long-term memory prediction,In ICIP-97,Santa Barbara,CA,USA,volume 11,pages 53-56,26-29 October,1997.
[23].Kui Zhang and J.Kittler,Multiple-thread long-term memory scheme for efficient video coding,ELECTRONICS LETTERS 79th August 1999 Vol.35No.77.
[24]. K. Zhang and J. Kittler, Using scene-change detection and multiplethread background memory for efficient video coding, ELECTRONICS LETTERS 18th February 1999 Vol. 35 No. 4.
[25]. Rong Ding, Qionghai Dai, Wenli Xu, Dongdong Zhu and Hao Yin, Background-frame Based Motion Compensation for Video Compression, 2004 IEEE International Conference on Multimedia and Expo (ICME).
[26]. Dong-Hyuk Shin, Rae-Hong Park, Seungjoon Yang, and Jae-Han Jung, Block-Based Noise Estimation Using Adaptive Gaussian Filtering, IEEE Transactions on Consumer Electronics, Vol. 51, No. 1, FEBRUARY 2005.
[27]. Ys. Fong, CA pom alaza Rxes and XH wang. Comparison Study of Nonlinear Filters in Image Processing Application. Opt Engng, 1989, 28(7):749-760.
[28]. Fabrizio Russo, A Method for Estimation and Filtering of Gaussian Noise in Images, IEEE Transactions on Consumer Electronics, Vol. 49, No. 3, AUGUST 2003.
[29]. Kim, S.D., and Ra, J.B, Efficient block-based video encoder embedding a Wiener filter for noisy video sequences, J. Vis. Commun. Image Represent., 2003, 14,(1), pp. 22-40.
[30]. B.C. Song and K.W. Chun, Motion-compensated temporal filtering for denoising in video encoder, ELECTRONICS LETTERS 24th June 2004 Vol. 40 No. 13.
[31]. Fengling Li and Nam Ling, Improved Content Adaptive Update Weight Control in Motion-Compensated Temporal Filtering, ISCAS 2006.
[32]. R. Dugad and N.Ahuja, Video denoising by combining Kalman and Wiener estimates, in Proc. IEEE Intl. Conf. on Image Process., Kobe, Japan, Oct 1999, vol. 4, pp. 156-159.
[33]. A. Secker and D. Taubman, Highly scalable video compression update step algorithm, which makes use of motion vector motion compensation, Proc. IEEE ICIP, vol. 3, pp.24-28, Jun. 2002.
[34]. B. Girod and S. Han, Optimum update for motion-compensated lifting, IEEE Signal Process.Lett.,vol.12,pp.150-153,Feb.2005.
[35].Liwei Guo,Oscar C.Au,Mengyao Ma and Zhiqin Liang,An Encoder-Embeded video denoising filter based on the temporal LMMSE estimator,IEEE ICME 2006.
[36].Boo,K.J.,and Bose,N.K,A Motion-compensated spatio-temporal filter for image sequences with signal-dependent noise,IEEE Trans.Circuits Syst.Video Technol.,1998,8,(3),pp.287-298.
[37].Hai Bing Yin,Xiang Zhong Fang,Zhang Wei,and X.K.Yang,An Improve Motion-Compensated 3-D LLMMSE Filter With Spatio-Temporal Adaptive Filtering Support,IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,VOL.17,NO.12,DECEMBER 2007.
[38].T.Viero and Y.Neuvo,3-D median structures for image sequence filtering and coding,in Motion Analysis and Image Sequence Processing,M.I.Sezan and R.L.Lagendijk,Eds.Norwell,MA:Kluwer,1993,pp.411-445.
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