分布式视频编码关键技术研究
|
摘要
随着网络技术、无线技术和计算机技术的飞速发展,近年来涌现出许多具有崭新特点的多媒体应用设备,如无线视频传感器监控网络、移动摄像手机和便携式摄像机等。它们在存储容量、计算能力和功率资源等方面都受到很大的限制,需要简单的编码器以节省资源。传统的视频编码方案如H.264由于编码复杂度高的特性已不能满足其应用需求。分布式视频编码系统是一种不同于传统混合视频编码的视频编码体系,它将耗时耗功率的运动估计／补偿从编码端移到解码端,具有编码简单、解码相对复杂以及容错性能较好的特点,为以上应用场合提供了很好的解决方案。本文研究分布式视频编码的相关理论和关键技术,主要内容和创新点如下：
1.边信息生成技术是分布式视频编码系统中的关键技术之一,它的质量的好坏影响着系统的率失真性能。本文提出了一种基于卡尔曼滤波的边信息生成方法。用卡尔曼滤波对运动估计过程中产生的运动矢量进行精化,提高运动矢量的准确度,然后将卡尔曼滤波和前向运动估计、双向运动估计以及空间平滑相结合生成边信息。仿真结果表明,与末采用卡尔曼滤波之前相比,边信息性能提高了。同时,将自适应卡尔曼滤波运用到运动估计中,进一步提高了边信息的性能。
2.小波变换具有时-频局部分析以及多分辨率分解特性,且能克服离散余弦变换变换带来的块效应。本文对小波域分布式视频编码系统进行了研究,并提出了一种小波域分布式视频编码系统的改进方案,对小波变换系数进行格雷码编码,能够减小Wyner-Ziv (WZ)帧和边信息之间的相关噪声误差。同时采用了更有效的虚拟信道模型和边信息生成技术。已解码的WZ帧含有当前帧的信息,利用已解码WZ帧信息对初始边信息进行精化,采用融合技术生成新的边信息辅助LDPC解码器重新解码重建WZ帧。改进的边信息能明显提高重建视频的质量。仿真结果表明,与传统的分布式视频编码系统相比,新的改进方案系统性能获得了较好的率失真性能。
3.目前研究提高分布式视频编码系统性能的主要工作是如何提高WZ帧的编码效率。研究表明,WZ帧采用多种编码模式进行编码,能够提高系统的率失真性能。首先从理论上分析了将WZ帧分类独立编码的可行性,接着提出了一种基于棋盘结构分类的分布式视频编码方案。在编码端,按照棋盘格式将WZ帧分成两部分进行独立编码。在解码端,采用三维递归搜索运动估计算法产生初始边信息,进而重建WZ帧的第一部分,接着用时空边界匹配算法对WZ帧的第二部分对应的边信息进行运动补偿精化,辅助解码WZ帧。仿真结果表明,与传统的分布式视频编码系统相比,提出的方案虽然增加了一些解码延迟,但是有效地提高了系统的率失真性能。
4.视频监控作为加强公共安全和保护隐私方面的重要工具,被广泛使用。本文针对视频监控这一应用场景,结合分布式信源编码的思想,提出了一种有效的低延迟分布式视频编码方案。鉴于在监控系统中运动剧烈的视频图像不会频繁出现,在编码端采用一种基于时空相关性的WZ帧编码模式判决方法,利用SKIP模式减小码率；在解码端采用基于Lucas-Kanade算法的边信息外推方法,实现了系统的顺序解码。这种边信息外推方法运算复杂度较高,但是运动矢量估计非常精确。仿真结果表明,与传统的基于外推的分布式视频编码系统相比,提出的方案系统率失真性能明显提升。
As the rapid development of the network technology, the wireless technology and the computer technology, many multimedia applications have emerged in recent years, such as the wireless video sensor network monitoring, mobile camera-phones and portable video camera and so on. As they are severely constrained in the computing capacity, memory and power, a simpler encoder is needed to conserve resources in the aforementioned multimedia scenarios. The traditional video coding schemes can not meet the requirements because of their high coding comlexity. Distributed Video Coding (DVC) is different from the traditional video coding. The time-consuming motion estimation/motion compensation procedure is shifted from the encoder to the decoder in the DVC. DVC has the characteristic of simple encoder, complex decoder and error resilience performance, which can provide excellent solutions for the applications above. This dissertation concerns with the theory and the key technology of DVC. The specific work and innovations of this dissertation can be summarized as follows:
1. The generation technology of side information, whose quality affects the rate-distortion performance of the system, is one of key techniques of DVC system. A generation method of side information based on Kalman filter is discussed in this dissertation. The Kalman filter is used to refine motion vector in the motion estimation process, and improve the accuracy of the motion vector. Then Kalman filter is combined with forward motion estimation, bi-directional motion estimation and spatial smoothing to generate the side information. The simulation results show that the performance of side information becomes better than that without using the Kalman filter. At the same time, when the adaptive Kalman filter is applied to the motion estimation, the performance of side information can be further improved.
2. Wavelet transform has the properties of local time-frequency analysis and multiresolution decomposition, and can overcome the blocking effect caused by discrete cosine transform. Therefore, the DVC scheme wavelet-domain is studied in this dissertation, and an improved DVC scheme is proposed. The Gray code is used to encode coefficient after wavelet transform, and it can reduce the correlation noise errors between the WZ frame and side information. Meanwhile, more effective virtual channel model and generation technology of side information are adopted. A decoded WZ frame contains the information of current frame, so it is used to refine initial side information. The fusion technology is adopted to generate a new side information which can assist LDPC decoder to decode re-reconstruction WZ frame. The new side information can significantly improve the quality of the video reconstruction. The simulation results show that the performance of the proposed scheme is better than that of the traditional DVC.
3. Now, the main work of the study on improving the performance of DVC system is how to improve the coding efficiency of WZ frame. It can improve the performance of the system that WZ frame is encoded to use the many kinds of coding models. Firstly the feasibility of classifying WZ frame to encode independently is theoretically analyzed, and then a DVC scheme based on the chessboard pattern is proposed.
At the encoder, each WZ frame is split into two sets according to chessboard pattern and then encoded separately. At the decoder side, the3D recursive search motion estimation algorithm is adopted to generate the initial side information, and then first part of WZ frame is reconstructed. Secondly, spatio-temporal boundary matching algorithm is used in motion compensation refinement for the side information corresponding to the second part of WZ frame. The simulation results show that the proposed scheme has better rate-distortion performance than that of the traditional DVC system.
4. As an important tool for strengthening the public security and privacy protection, video monitoring is widely used. For the scenarios of video monitoring, combined with the ideas of distributed source coding, an effective DVC scheme with low delay for the scenarios of video monitoring is proposed in the dissertation. In fact, the video image with violent movement will not appear frequently in the video monitoring system. At the encoder, a coding mode decision method of WZ frame based on temporal correlation and spatial correlation is adopted, and SKIP mode is used for reducing the bit rate. At the decoder, Side information extrapolation technique based on Lucas-Kanade algorithm is adopted to achieve sequential encoding of system. While its computing complexity is higher, motion vector estimation is more accurate. The simulation results show that the proposed scheme can significantly improve the rate-distortion performance of the system compared with the traditional scheme based on extrapolation.
1.边信息生成技术是分布式视频编码系统中的关键技术之一,它的质量的好坏影响着系统的率失真性能。本文提出了一种基于卡尔曼滤波的边信息生成方法。用卡尔曼滤波对运动估计过程中产生的运动矢量进行精化,提高运动矢量的准确度,然后将卡尔曼滤波和前向运动估计、双向运动估计以及空间平滑相结合生成边信息。仿真结果表明,与末采用卡尔曼滤波之前相比,边信息性能提高了。同时,将自适应卡尔曼滤波运用到运动估计中,进一步提高了边信息的性能。
2.小波变换具有时-频局部分析以及多分辨率分解特性,且能克服离散余弦变换变换带来的块效应。本文对小波域分布式视频编码系统进行了研究,并提出了一种小波域分布式视频编码系统的改进方案,对小波变换系数进行格雷码编码,能够减小Wyner-Ziv (WZ)帧和边信息之间的相关噪声误差。同时采用了更有效的虚拟信道模型和边信息生成技术。已解码的WZ帧含有当前帧的信息,利用已解码WZ帧信息对初始边信息进行精化,采用融合技术生成新的边信息辅助LDPC解码器重新解码重建WZ帧。改进的边信息能明显提高重建视频的质量。仿真结果表明,与传统的分布式视频编码系统相比,新的改进方案系统性能获得了较好的率失真性能。
3.目前研究提高分布式视频编码系统性能的主要工作是如何提高WZ帧的编码效率。研究表明,WZ帧采用多种编码模式进行编码,能够提高系统的率失真性能。首先从理论上分析了将WZ帧分类独立编码的可行性,接着提出了一种基于棋盘结构分类的分布式视频编码方案。在编码端,按照棋盘格式将WZ帧分成两部分进行独立编码。在解码端,采用三维递归搜索运动估计算法产生初始边信息,进而重建WZ帧的第一部分,接着用时空边界匹配算法对WZ帧的第二部分对应的边信息进行运动补偿精化,辅助解码WZ帧。仿真结果表明,与传统的分布式视频编码系统相比,提出的方案虽然增加了一些解码延迟,但是有效地提高了系统的率失真性能。
4.视频监控作为加强公共安全和保护隐私方面的重要工具,被广泛使用。本文针对视频监控这一应用场景,结合分布式信源编码的思想,提出了一种有效的低延迟分布式视频编码方案。鉴于在监控系统中运动剧烈的视频图像不会频繁出现,在编码端采用一种基于时空相关性的WZ帧编码模式判决方法,利用SKIP模式减小码率；在解码端采用基于Lucas-Kanade算法的边信息外推方法,实现了系统的顺序解码。这种边信息外推方法运算复杂度较高,但是运动矢量估计非常精确。仿真结果表明,与传统的基于外推的分布式视频编码系统相比,提出的方案系统率失真性能明显提升。
As the rapid development of the network technology, the wireless technology and the computer technology, many multimedia applications have emerged in recent years, such as the wireless video sensor network monitoring, mobile camera-phones and portable video camera and so on. As they are severely constrained in the computing capacity, memory and power, a simpler encoder is needed to conserve resources in the aforementioned multimedia scenarios. The traditional video coding schemes can not meet the requirements because of their high coding comlexity. Distributed Video Coding (DVC) is different from the traditional video coding. The time-consuming motion estimation/motion compensation procedure is shifted from the encoder to the decoder in the DVC. DVC has the characteristic of simple encoder, complex decoder and error resilience performance, which can provide excellent solutions for the applications above. This dissertation concerns with the theory and the key technology of DVC. The specific work and innovations of this dissertation can be summarized as follows:
1. The generation technology of side information, whose quality affects the rate-distortion performance of the system, is one of key techniques of DVC system. A generation method of side information based on Kalman filter is discussed in this dissertation. The Kalman filter is used to refine motion vector in the motion estimation process, and improve the accuracy of the motion vector. Then Kalman filter is combined with forward motion estimation, bi-directional motion estimation and spatial smoothing to generate the side information. The simulation results show that the performance of side information becomes better than that without using the Kalman filter. At the same time, when the adaptive Kalman filter is applied to the motion estimation, the performance of side information can be further improved.
2. Wavelet transform has the properties of local time-frequency analysis and multiresolution decomposition, and can overcome the blocking effect caused by discrete cosine transform. Therefore, the DVC scheme wavelet-domain is studied in this dissertation, and an improved DVC scheme is proposed. The Gray code is used to encode coefficient after wavelet transform, and it can reduce the correlation noise errors between the WZ frame and side information. Meanwhile, more effective virtual channel model and generation technology of side information are adopted. A decoded WZ frame contains the information of current frame, so it is used to refine initial side information. The fusion technology is adopted to generate a new side information which can assist LDPC decoder to decode re-reconstruction WZ frame. The new side information can significantly improve the quality of the video reconstruction. The simulation results show that the performance of the proposed scheme is better than that of the traditional DVC.
3. Now, the main work of the study on improving the performance of DVC system is how to improve the coding efficiency of WZ frame. It can improve the performance of the system that WZ frame is encoded to use the many kinds of coding models. Firstly the feasibility of classifying WZ frame to encode independently is theoretically analyzed, and then a DVC scheme based on the chessboard pattern is proposed.
At the encoder, each WZ frame is split into two sets according to chessboard pattern and then encoded separately. At the decoder side, the3D recursive search motion estimation algorithm is adopted to generate the initial side information, and then first part of WZ frame is reconstructed. Secondly, spatio-temporal boundary matching algorithm is used in motion compensation refinement for the side information corresponding to the second part of WZ frame. The simulation results show that the proposed scheme has better rate-distortion performance than that of the traditional DVC system.
4. As an important tool for strengthening the public security and privacy protection, video monitoring is widely used. For the scenarios of video monitoring, combined with the ideas of distributed source coding, an effective DVC scheme with low delay for the scenarios of video monitoring is proposed in the dissertation. In fact, the video image with violent movement will not appear frequently in the video monitoring system. At the encoder, a coding mode decision method of WZ frame based on temporal correlation and spatial correlation is adopted, and SKIP mode is used for reducing the bit rate. At the decoder, Side information extrapolation technique based on Lucas-Kanade algorithm is adopted to achieve sequential encoding of system. While its computing complexity is higher, motion vector estimation is more accurate. The simulation results show that the proposed scheme can significantly improve the rate-distortion performance of the system compared with the traditional scheme based on extrapolation.
引文
[1]ITU-T and ISO/IEC JTC1. Advanced Video Coding for Generic Audiovisual Services. ITU-T Recommendation H.264-ISO/IEC 14496-10 AVC,2003.
[2]ITU-T and ISO/IEC JTC1. Advanced Video Coding for Generic Audiovisual Services. ITU-T Recommendation H.264-ISO/IEC 14496-10 AVC,2005.
[3]ITU-T and ISO/IEC JTC1. Advanced video coding for generic audiovisual services Amendment 1:Support of additional colour spaces and removal of the High 4:4:4 Profile. ITU-T Recommendation H.264(2005)-Amendment 1,2005.
[4]Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG. Joint Draft ITU-T Rec. H.264| ISO/IEC 14496-10/Amd.3 Scalable video coding. JVT-X201, July, 2007.
[5]Joint Collaborative Team-Video Coding. Test Model under Consideration. JCTVC-A205. Dresden, Germany, April,2010:15-23. [Online]. http://wftp3.itu.int/av-arch/jctvc-site/2010_04_A_Dresden/JCTVC-A205.zip
[6]Joint Collaborative Team-Video Coding. [Online]. http://phenix.int-evry.fr/jct/index.php
[7]D. Marpe, T. Wiegand, and G. J. Sullivan. The H.264/MPEG4 advanced video coding standard and its applications. IEEE Communications Magazine,44(8), 2006:134-143.
[8]F. Pereira, L. Torres, C. Gullemot, et al. Distributed video coding:selecting the most promising application scenarios. Elsevier Journal Signal Processing:Image Communication,23(1),2008:339-352.
[9]宗晓飞.信源网络联合编码关键技术研究及应用[学位论文],北京,北京邮电大学,2009.
[10]ISO/IEC. Information technology-coding of moving pictures and associated audio for digital storage media at up to about 1.5Mb/s, Part 1:system, Part 2: video, Part 3:audio, IS 11172,1993.
[11]TU-T and ISO/IEC. Generic coding of moving pictures and associated audio information-Part 2:video, ITU-T Rec. H.262 and ISO/IEC 13818-2 (MPEG-2 Video),1994.
[12]ITU-T. Video coding for low bitrate communication, Rec. H.263.1995.
[13]ITU-T. Video coding for low bitrate communication, Rec. H.263 Version 2, 1998.
[14]ITU-T, Enhanced reference picture selection mode, Rec. H.263 Annex U,2000.
[15]ISO/IEC. Generic coding of audio-visual objects-part 2:visual,14496-2,1999.
[16]ITU-T, Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services, Rec. H.264 and ISO/IEC 14496-10 Version 1, May 2003.
[17]ITU-T. Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services. Rec. H.264 and ISO/IEC 14496-10 Version 3, Mar.2005.
[18]ITU-T. Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services. Rec. H.264 and ISO/IEC 14496-10 Version 8, Nov.2007.
[19]ITU-T. Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services. Rec. H.264 and ISO/IEC 14496-10 Version 11, Mar.2009.
[20]David R, Bull. Edward J. Delp, and Seishi Takamura. Introduction to the issue on emerging technologies for video compression. IEEE Journal of Selected Topics Signal Processing,5(7), Nov.2011:1277-1281.
[21]Joint Collaborative Team. Video Coding. Geneva, Switzerland, May,2012. [Online]. http://phenix.int-evry.fr/jct/index.php
[22]http://www.cnblogs.com/OMG-Team/archive/2011/11/03/2234917.html
[23]常侃.H.264的关键技术研究[学位论文],北京,北京邮电大学,2009.
[24]毕厚杰.新一代视频压缩编码标准——H.264/AVC,北京,人民邮电出版社,ISBN 7-115-13064-7,2009.
[25]韩钰.基于纹理合成的图像编码算法[学位论文].北京,北京邮电大学,2010.
[26]J. H. Min and Samsung. Unification of the directional intra prediction methods in TMuC. JCTVC-B100, July,2010.
[27]K. McCann, W. J. Han,I. K. Kim, et al. Samsung's Response to the call for proposals on video compression technology. JCTVC-A124, Apr.,2010.
[28]K. Ugur. K. R. Andersson. and A. Fuldseth. Description of video coding technology proposal by Tandberg. Nokia, Ericsson, JCTVC-A119, Apr.,2010.
[29]JCT-VC. High efficiency video coding (HEVC) text specification draft 6. JCTVC-H1003, Feb.,2012.
[30]K. Mccann, S. Sekiguci, B. Bross. et al. HEVC test model 3 (HM3) encoder description, JCTVC-E602,2011.
[31]T. Tan, W. Han, B. Bross, et al. Bog report of CE9:motion vector coding, JCTVC-D441,2011.
[32]Y. Huang, B. Bross, M. Zhou, et al. CE9:summary report of core experiment on MV coding and skip/merge operations, JCTVC-F029,2011.
[33]T. Wiegand, B. Bross, W. Han, et al. WD3:working draft 3 of high-efficiency video coding, JCTVC-E603,2011.
[34]S. Chong, M. Karczewicz, C. Chen, et al. CE8 subtest2:block based adaptive loop filter, JCTVC-E323,2011.
[35]C. Chen, C. Fu, C.Tsai, et al. CE8 subtest2:adaptation between pixel-based and region-based filter selection, JCTVC-E046,2011.
[36]R. Cohen, A. Vetro. CE7:Cross-verification of Samsung's (JCTVC-D357) Fast Rotational Transform. JCTVC-D030, Daegu, Korea, Oct.2011.
[37]D. Marpe, H. Schwarz, and T. Wiegand. Entropy coding in video compression using probability interval partitioning. Picture Coding Symposium, PCS 2010, Japan, Dec.2010:66-69.
[38]M. Budagavi and M. Demircin. Parallel context processing techniques for high coding efficiency entropy coding in HEVC, JCTVC-B088,2010.
[39]D. Slepian and J. Wolf. Noiseless coding of correlated information sources. IEEE Transactions on Information Theory,19(4), July,1973:471-480.
[40]A. Wyner and J. Ziv. The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory,22(1), Jan., 1976:1-10.
[41]A. Wyner. Recent results in the Shannon theory. IEEE Transactions on Information Theory,20(1), Jan.,1974:2-10.
[42]S. S. Pradhan and K. Ramchandran. Distributed source coding using syndromes (DISCUS):Design and construction. Data Compression Conference, DCC1999, Snowbird, UT. Mar.1999:158-167.
[43]X. Wang and M. Orchard. Design of trellis codes for source coding with side information at the decoder. Data Compression Conference, DCC2001, Snowbird. UT. Mar.2001:361-370.
[44]A. Aaron. Distributed source coding. [online]. http://ivms.stanford.edu/%7Eamaaaron/dsc/index.htm
[45]C. Berrou, A. Glavieux, and P. Thitimajshima. Near shannon limit error correcting coding and decoding:turbo-codes. IEEE International Conference on Communication, ICC 1993, Geneva, Switzerland, May 1993:1064-1070.
[46]A. Aaron and B. Girod. Compression with side information using turbo codes. Data Compresssion Conference, DCC2002. Snowbird, UT, Apr.,2002:252-261.
[47]D. Varodayan, A. Aaron, and B. Girod. Rate-adaptive codes for distributed source coding. EURASIP Signal Processing Journal. Special Section on Distributed Source Coding,86(11), Nov.2006:3123-3130.
[48]Q. Xu, V. Stankovic, and Z. Xiong. Distributed joint source-channel coding of video using raptor codes. IEEE Journal on Selected Areas in Communications, 25(4), May 2007:851-861.
[49]R. Zamir. The rate loss in the Wyner-ziv problem. IEEE Transactions on Information Theory,42(6), Nov.,1996:2073-2084.
[50]S. S. Pradhan, J. Chou, and K. Ramchandran. Duality between source coding and channel coding and its extension to the side information case. IEEE Transactions on Information Theory,49(5), May,2003:1181-1203.
[51]M. Fleming, Q. Zhao, and M. Effros. Network vector quantization. IEEE Trans. Inf. Theory,50(8), Aug.2004:1584-1604.
[52]M. Fleming and M. Effros. Network vector quantization. Data Compression Conference, DCC2001,2001:13-22.
[53]D. Muresan and M. Effros. Quantization as histogram segmentation:globally optimal scalar quantizer design in network systems. Data Compression Conference, DCC2002,2002:302-311.
[54]D. Rebollo-Monedero, R. Zhang, and B. Gird. Design of optimal quantizers for distributed source coding. Data Compression Conference, DCC2003, Snowbird, UT, Mar.2003:13-22.
[55]D. Rebollo-Monedero, A. Aaron, and B. Gird. Transforms for high-rate distributed source coding. The Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA,2003:850-854.
[56]A. Aaron, S. Rane, E. Setton, et al. Transform-domain Wyner-Ziv codec for video. SPIE Conference on Visual Communications and Image Processing,2004: 520-528.
[57]X. Guo, Y. Lu, F. Wu, et al. Wyner-Ziv video coding based on set partitioning in hierarchical tree. Proc. IEEE International Conference on Image Processing, ICIP2006,2006:601-604.
[58]M. Gastpar, P. Dragotti, and M. Vetterli. The distributed Karhunen-Loeve transform. IEEE Transactions on Information Theory,52(12),2006:5177-5196.
[59]A. Aaron, R. Zhang, and B. Girod. Wyner-Ziv coding of motion video, Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Nov.2002:240-244.
[60]B. Girod, A. Aaron, S. Rane, et al. Distributed video coding. Proceedings of the IEEE,93(1), Jan.2005:71-83.
[61]R. Puri and K. Ramchandran. PRISM:a new robust video coding architecture based on distributed compression principles. Annual Allerton Conference on Communication, Control and Computing, IL, Allerton, Oct.2002.
[62]R. Puri, A. Majumdar, and K. Ramchandran. PRISM:a video coding paradigm with motion estimation at the decoder. IEEE Transactions on Image Processing, 16(10), Oct.2007:2436-2448.
[63]X. Artigas, J. Ascenso, M. Dalai, et al. The DISCOVER Codec:architecture, techniques and evaluation. Picture Coding Symposium, PCS2007, Lisbon, Portugal, Nov.2007.
[64]J. Ascenso, C. Brites, and F. Pereira. Design and performance of a novel low density parity check code for distributed video coding. IEEE International Conference on Image Processing, ICIP2008, San Diego, CA, Oct. 2008:1116-1119.
[65]J. Ascenso, C. Brites, and F. Pereira. A denoising approach for iterative side information creation in distributed video coding. IEEE International Conference on Image Processing, ICIP2011, Brussels, Belgium,2011:3513-3516.
[66]A. Abou-Elailah, J. Farah, M. Cagnazzo, et al. Improved side information generation for distributed video coding. European Workshop on Visual Information Processing,2011:42-49.
[67]J. Ascenso, C. Brites, and F. Pereira. A flexible side information generation framework for distributed video coding. Multimedia Tools and Applications, 48(3), Jul.2010:381-409.
[68]Song Juan, Wang Keyan, Liu Haiying, et al. Progressive correlation noise refinement for transform domain Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2011, Brussels, Belgium,2011: 2625-2628.
[69]N. Deligiannis, J. Barbarien, M. Jacobs, et al. Side-Information-Dependent correlation channel estimation in hash-based distributed video coding. IEEE Transactions on Image Processing,21(4),2012:1934-1949.
[70]Liu Hongbin, Li Yongpeng, Liu Xianming, et al. Two-pass reconstruction in distributed video coding. Picture Coding Symposium, PCS2009,2009:1-4.
[71]Zhang Yongsheng, Xiong Hongkai, He Zhihai, et al. Reconstruction for distributed video coding:a context-adaptive markov random field approach. IEEE Transactions on Circuits and Systems for Video Technology,21(8),2011: 1100-1114.
[72]M.Tagliasacchi, J. Pedro, F. Pereira, et al. An efficient request stopping method at the turbo decoder in distributed video coding. European Signal Processing Conference, Poznan, Poland, Sept.2007.
[73]Maugey T., Yaacoub C., Farah J., et al. Side information enhancement using an adaptive hash-based genetic algorithm in a Wyner-Ziv context.2010 IEEE International Workshop on ultimedia Signal Processing, MMSP2010,2010: 298-302.
[74]Li Zhihong, Wng Anhong, and Wang Haifang. Distributed video coding based on conditional entropy hash. International Conference on Computational Aspects of Social Networks.2010:382-385.
[75]T. Sheng, G. Hua, H. Guo, et al. Rate alloeation for transform domain Wyner-Ziv video coding without feedback. ACM International Conference on Multimedia. Canada,2008,701-704.
[76]Ye Feng, Men Aidong. Xiao He, et al. Feedback-free dietributed video coding using parallelized design. Picture Coding Symposium, PCS2012. May 2012.
[77]H. Wang, N. Cheung, and A. Ortega. A framework for adaptive scalable video coding using Wyner-Ziv techniques. EURASIP Journal on Applied Signal Processing,2006:1-18.
[78]Q. Xu and Z. Xiong. Layered Wyner-Ziv Video Coding. IEEE Trans. on Image Processing.15(12), Dec.2006:3791-3803.
[79]J. Q. Pedro, L. D. Soares, C. Brites, et al. Studying error resilience performance for a feedback channel based transform domain Wyner-Ziv video codec. Picture Coding Symposium, PCS2007, Lisbon, Portugal, Nov.2007.
[80]Zhang Yongsheng, Xiong Hongkai, He Zhihai, et al. An error resilient video coding scheme using embedded Wyner-Ziv description with decoder side non-stationary distortion modeling. IEEE Transactions on Circuits and Systems for Video Technology,21(4),2011:498-512.
[81]J. Ascenso, C. Brites, and F. Pereira. Improving frame interpolation with spatial motion smoothing for pixel domain distributed video coding. EURASIP Conference on Speech and Image Processing, Multimedia Communications and Services, Smolenice, Slovak Republic, July 2005.
[82]L. Natario, C. Brites, J. Ascenso, et al. Extrapolating side information for low-delay pixel-domain distributed video coding. International Workshop on Very Low Bit rate Video Coding, Sardinia, Italy, Sept.2005:16-21.
[83]A. Aaron, S. Rane, B. Girod. Wyner-Ziv video coding with hash-based motion compensation at the receiver. IEEE International Conference on Image Processing, ICIP2004, Singapore, Oct.2004:3097-3100.
[84]N. Deligiannis, M. Jacobs, F. Verbist, et al. Efficient hash-driven Wyner-Ziv video coding for visual sensors. IEEE International Conference on Distributed Smart Cameras, ICDSC2011,2011,1-6.
[85]S. Klomp, Y. Vatis, and J. Ostermann. Side information interpolation with sub-pel motion compensation for Wyner-Ziv decoder. International Conference on Signal Processing and Multimedia Applications, Setubal, Portugal, Aug., 2006.
[86]Liu Rongke, Yue Zhia, and Chen Changwen. Side information generation based on hierarchical motion estimation in distributed video coding. Chinese Journal of Aeronautics,22(2),2009:167-173.
[87]X. Artigas and L. Torres. Iterative generation of motion-compensated side information for distributed video coding. IEEE International Conference on Image Processing, ICIP2005, Genova, Italy, Sept.2005:833-836.
[88]M. O. Akinola, L. S. Dooley, and P. K. C. Wong. Wyner-Ziv side information generation using a higher order piecewise trajectory temporal interpolation algorithm. International Conference on Graphic and Image Processing, Manila, Philippines, Dec.2010.
[89]M. O. Akinola, L. S. Dooley, P. K. C. Wong. Improved side information generation using adaptive overlapped block motion compensation and higher-order interpolation. International Conference on Systems. Signals and Image Processing,2011:1-4.
[90]C. Yaacoub, J. Farah, and B. Pesquet-Popescu. Agenetic algorithm for side information enhancement in distributed video coding. IEEE International Conference on Image Processing, ICIP2009, Cairo, Egypt, Nov.2009: 2933-2936.
[91]X. Fan, O. Au, N. Cheung, et al. Successive refinement based Wyner-Ziv video compression. Signal Processing:Image Communication., vol.25, Jan.2010: 47-63.
[92]P. Meyer, R. Westerlaken, R. Gunnewiek, et al. Distributed source coding of video with non-stationary side information. SPIE Conference on Visual Communications and Image Processing,5960,2005:857-866.
[93]M. Dalai, R. Leonardi and F. Pereira. Improving turbo codec integration in pixel-domain distributed video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2006,2006.
[94]C. Brites and F. Pereira. Correlation noise modeling for efficient pixel and transform domain Wyner-Ziv video coding. IEEE Transactions on Circuits and Systems for Video Technology,18(9),2008:1177-1190.
[95]X. Fan, O. Au, and N. Cheung. Adaptive correlation estimation for general Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2009,2009:1409-1412.
[96]N. Deligiannis, A. Munteanu, T. Clerckx, et al. Modeling the correlation noise in spatial domain distributed video coding. Data Compression Conference, DCC2009,2009:443.
[97]L. Stankovic, V. Stankovic, S. Wang, et al. Distributed video coding with particle filtering for correlation tracking. European Signal Processing Conference, Aug.2010.
[98]Wang Shuang, Cui Lijuan, Lina Stankovic, et al. Adaptive correlation estimation with particle filtering for distributed video coding. IEEE Transactions on Circuits and Systems for Video Technology, PP(99),2011:1-10.
[99]C. Brites, J. Ascenso, J. Q. Pedro, et al. Evaluating a feedback channel based transform domain Wyner-Ziv video codec. Signal Processing:Image Communucation,23(4), Apr.2008:269-297.
[100]D. Kubasov, K. Lajnef, and C. Guillemot. A hybrid encoder/decoder rate control for Wyner-Ziv video coding with a feedback channel. IEEE Workshop on Multimedia Signal Processing, MMSP2007. Chania, Crete, Greece, Oct.2007.
[101]J. D. Areia. J. Ascenso, C. Brites, et al. Low complexity hybrid rate control for lower complexity Wyner-Ziv video decoding. European Signal Proceeding Conference, Lausanne, Switzerland, Aug.2008.
[102]X. Artigas and L. Torres. Improved signal reconstruction and return channel suppression in distributed video coding systems. ELMAR, Zadar, Croatia, Jun. 2005:53-56.
[103]M. Morbee, J. Prades-Nebot, A. Pizurica, et al. Rate allocation algorithm for pixel-domain distributed video coding without feedback channel. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP207, Honolulu, HI, USA, Apr.2007.
[104]M. Morbee, J. Prades-Nebot, A. Roca, et al. Improved pixel-based rate allocation for pixel-domain distributed video coders without feedback channel. chapter in Lecture Notes in Computer Science. Advanced Concepts for Intelligent Vision Systems, Vol.4678, Aug.2007:663-674.
[105]J. L. Martinez, G. Fernandez-Escribano, H. Kalva, et al. Feedback free DVC architecture using machine learning. IEEE International Conference on Image Processing, ICIP2008, San Diego, CA, USA, Oct.2008:1140-1143.
[106]T. Sheng, X. Zhu, G. Hua, et al. Feedback-free rate-allocation scheme for transform domain Wyner-Ziv video coding. Multimedia Systems,16(2),2010: 127-137.
[107]C. Brites and Pereira F. An efficient encoder rate control solution for transform domain Wyner-Ziv video coding. IEEE Transactions on Circuits and Systems for Video Technology,21(9),2011:1278-1292.
[108]P. Ishwar, V. M. Prabhakaran, and K. Ramchandran. Towards a theory for video coding using distributed compression principles. IEEE International Conference on Image Processing, ICIP2003, Barcelona, Catalonia, Spain, Sept. 2003:687-690.
[109]G. R. Esmaili and P. Cosman, Correlation noise classification based on matching success for transform domain Wyner-Ziv video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2009, Apr.2009:801-804.
[110]R. Westerlaken, S. Borchert, R. Gunnewiek, et al. Dependency channel modeling for a ldpc-based wyner-ziv video compression scheme. IEEE International Conference on Image Processing, ICIP2006,2006:277-280.
[111]M. Cagnazzo, T. Maugey, and B. Pesquet-Popescu. A differential motion estimation method for image interpolation in distributed video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2009, Taipei Taiwan,2009:1861-1864.
[112]M. Cagnazzo, W. Miled, T. Maugey, et al. Image interpolation with edge-preserving differential motion refinement. IEEE International Conference on Image Processing, ICIP2009,2009:361-364.
[113]G. Petrazzuoli, M. Cagnazzo and B. Pesquet-Popescu. High order motion interpolation for side information improvement in DVC. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2010, Dallas Texas USA,2010:2342-2345.
[114]J. Ascenso, C. Brites, and F. Pereira. Motion compensated refinement for low complexity pixel based distributed video coding. IEEE International Conference on Advanced Video and Signal Based Surveillance, Como, Italy, Sept.2005.
[115]D. Varodayan, D. Chen, M. Flierl, et al. Wyner-Ziv coding of video with unsupervised motion vector learning. Signal Processing:Image Communication. 23(5), June 2008:369-378.
[116]I.H. Tseng and A. Ortega. Motion estimation at the decoder using maximum likelihood techniques for distributed video coding. Asilomar Conference on Signals. Systems and Computers, Pacific Grove, CA, USA, Nov.2005:756-760.
[117]Greg Welch and Gary Bishop. An introduction to the Kalman Filter. [online]. NC 2006, Chapel Hill. www.cs.unc.edu/-welch/kalman/kalmanlntro.html.
[118]C.M. Kuo, C.H. Hsieh, Y.D. Jou, et al. Motion estimation for video compression using Kalman filtering. IEEE Transactions on Broadcasting,42, 1996:110-116.
[119]Yi Luo and Mehmet Celenk. Motion estimation for video compression using Kalman filtering with adaptive adjustment. The International Conference on Visual Information Engineering,2008:612-617.
[120]J. E. Fowler, M. Tagliasacchi and B. pesquet Popescu. Wavelet based distributed source coding of video. [online]. http://signal.ee.bilk.ent.edu.tr/def-eyent/papers/crl535.pdf,2005.
[121]Wang Anhong, Zhao Yao, and Li Jing. Wavelet-Domain distributed video coding with motion-compensated refinement. IEEE International Conference on Image Processing, ICIP2006. Atlanta GA USA, Oct.2006:241-244.
[122]Guo Xun, Lu Yan, Wu Feng, et al. Distributed video coding using wavelet. IEEE International Symposium on Circuits and Systems,2006:5427-5430.
[123]Liu W., Dong L., and Zeng W. Estimating side-information for Wyner-Ziv video coding using resolution-progressive decoding and extensive motion exploration. IEEE International Conference on Acoustics. Speech, and Signal Processing, ICASSP2009, Taipei Taiwan, Apr.2009:721-724.
[124]R. Bernardini, R. Rinaldo, A. Vitali. et al. Performance evaluation of wavelet-based distributed video coding schemes, Signal, Image and Video Processing,5(1),2011:49-60.
[125]Li Xin. On the importance of source classification in Wyner-Ziv video coding. SPIE Conference on Visual Communicatio and Image Processig, San Jose, California, USA, Jan.2008.
[126]Zhang Jinrong, Li Houqiang, Liu Qiwei, et al. A transform domain classification based wyner-ziv video coding. International Conference on Multimedia and Expo, ICME2007,2007:144-147.
[127]R. Bernardini, R. Rinaldo, P. Zontone, et al. Wavelet domain distributed coding for video. IEEE International Conference on Image Processing, ICIP2006, Atlanta GA USA, Oct.2006:245-248.
[128]王安红.分布式视频编码研究[学位论文].北京,北京交通大学,2009.
[129]J. Oliver and M. P. Malumbres. Low-complexity multiresolution image compression using wavelet lower trees. Transactions on Circuits and Systems for Video Technology,16,2006:1437-1444.
[130]侯萌洁.分布式视频编码中的信道建模及边信息改进算法研究[学位论文].北京,北京邮电大学,2011.
[131]A. D. Liveris, Z. Xiong and C. N. Geoghiades. Compression of binary sources with side information using low-density parity-check codes. Global Telecommunications Conference,2002:1300-1304.
[132]W. J. Chien and L. Karam. BLAST-DVC:BitpLAne SelecTive distributed video coding. Multimedia Tools and Applications,48(3), Apr.2010:37-456.
[133]Z. Belkoura and T. Sikora. Improving Wyner-Ziv video coding by block-based distortion estimation. European Signal Processing Conference,2006.
[134]Feng Ying. Li Yunsong, Wu Chengke, et al. Coding scheme with skip mode based on motion filed detection for DVC. Processings of SPIE-the International Society for Opitical Engineering,2008.
[135]S. Mys, J. Slowack, J. Skorupa, et al. Introducing skip mode in distributed video coding. Signal Processing:Image Communication,24(3),2009:200-213.
[136]W. J. Chien, L. Karam, and G. Abousleman Block-adaptive wyner-ziv coding for transform-domain distributed video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2007,2007.
[137]T. Do, H. J. Shim and B. Jeon. Motion linearity based skip decision for Wyner-Ziv coding. International Conference on Computer Science and Information Technology,2009
[138]A. Trapanese, M. Tagliasacchi, S. Tubaro, et al. Embedding a block-based intra mode in frame-based pixel domain Wyner-Ziv video coding. International Workshop on Very Low Bitrate Video,2005.
[139]M. Tagliasacchi, A. Trapanese, S. Tubaro, et al. Intra mode decision based on spatio-temporal cues in pixel domain Wyner-Ziv video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2006,2006.
[140]C. TsaiD, M. LeeC, and N. LieW. Dynamic key block decision with spatio-temporal analysis for Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2007,2007.
[141]J. Ascenso and F. Pereira. Low complexity intra mode selection for efficient distributed video coding. International Conference on Multimedia and Expo, ICME2009,2009:101-104.
[142]G. Esmaili and P. Cosman. Low complexity spatio-temporal key frame encoding for Wyner-Ziv video coding. Data Compression Conference, DCC2009, 2009.
[143]S. Benierbah and M. Khamadja. Hybrid Wyner-Ziv and intra video coding with partial matching motion estimation at the decoder. IEEE International Conference on Image Processing, ICIP2009,2009:2925-2928.
[144]A. Tekalp. Digital Video Processing. Prentice-Hall, Englewood Cliffs, NJ, 1995.
[145]T. Koga, K. Iinuma, A. Hirano, et al. Motion compensated interframe coding for video conferencing. Proceedings of the National Telecommunications Conference.1981:C9.6.1-C9.6.5.
[146]L. Po and W. Ma. A novel four-step search algorithm for fast block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology,6 (3).1996:313-317.
[147]R. Li, B. Zeng, and M. Liou. A new three-step search algorithm for block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology,4 (4),1994:438-442.
[148]L. Liu, E. Feig. A block-based gradient descent search algorithm for block motion estimation in video coding. IEEE Transactions on Circuits and Systems for Video Technology,6(4),1996:419-422.
[149]C. Cheung and L. Po. A novel cross-diamond search algorithm for fast block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology,12 (12),2002:1168-1177.
[150]C. Cheung and L. Po. A novel small-cross-diamond search algorithm for fast video coding and video conferencing applications. IEEE International Conference on Image Processing, ICIP2002, Sept.2002:681-684.
[151]W. J. Chien, J. K. Lina, and P. A. Glen. Distributed video coding with 3-D recursive search block matching. IEEE International Symposium on Circuits and Systems, Puerto Rico, August 2006.
[152]Y. K. Wang, M. M. Hannuksela, V. Varsa, et al. The error concealment feature in the H.26L test model. IEEE International Conference on Image Processing, ICIP2002, Sept.2002:729-736.
[153]D. Agrafiotis, D. R. Bull, and C. N. Canagarajah. Enhanced error concealment with mode selection. IEEE Transactions on Circuits and Systems for Video Technology,16(8), Aug.2006:960-973.
[154]W. Zhu, Y. Wang and Q. F. Zhu. Second-order derivative- based smoothness measure for error concealment in DCT-based codecs. IEEE Transactions on Circuits and Systems for Video Technology,8(6),1998:713-718.
[155]S. Ye, X. Lin and Q. Sun. Content based error detection and concealment for image transmission over wireless channel. IEEE International Symposium on Circuits and Systems, vol.2, Bangkok, Thailand, May 2003:368-371.
[156]J. H. Zheng and L. P. Chau. A temporal error concealment algorithm for H.264 using Lagrange interpolation. IEEE International Symposium on Circuits and Systems,2004:133-136.
[157]W. N. Lie and Z. W. Gao. Video error concealment by intergrating greedy suboptimization and Kalman filtering techniques. IEEE Transactions on Circuits and Systems for Video Technology,16,2006:982-992.
[158]Y. Chen, X. Sun, F. Wu, et al. Spatio-temporal video error concealment using priority-ranked region-matching. IEEE International Conference on Image Processing, ICIP2005,2005:1050-1053.
[159]L. Atzori, N. De, F. G. B., et al. A spatio-temporal concealment technique using boundary matching algorithm and mesh-based warping (BMA-MBW). IEEE Transactions on Multimedia,3(3), Sep.2001:326-338.
[160]Hongbin Liu, Xiangyang Ji, Debin Zhao, et al. Distributed Video Coding using block based checkerboard pattern splitting algorithm. Picture Coding Symposium, PCS2007, Lisbon, Portugal, Nov.2007.
[161]A.Hampapur, L.Brown, J.Connell, et al. Smart video surveillance:Exploring the concept of multiscale spatio temporal tracking. IEEE Signal Processing Magazine,22(2), Mar.2005:38-51.
[162]W. Hu, T. Tan, L.Wang, et al. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews,34(3), Aug.2004:334-352.
[163]R.Kumar, H.Sawhney, S. Samarasekera, et al. Aerial video surveillance and exploitation. IEEE Processing,89(10), Oct.2001:1518-1539.
[164]T. Wiegand, G. J. Sullivan, G. Bjntegaard, et al. Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology,13(7), Jul.2003:560-576.
[165]P. Kumar, A. Pande, and A. Mittal. Efficient compression and network adaptive video coding for distributed video surveillance. Multimedia Tools and Applications,56, Dec.2010:365-384.
[166]Liu Limin, Li Zhen and E. J. Delp. Efficient and Low-Complexity Surveillance Video Compression Using Backward-Channel Aware Wyner-Ziv Video Coding. IEEE Transactions on Circuits and Systems for Video Technology, 19(4), Apr.2009:453-465.
[167]Liu Hongbin, Ma Siwei and Fan Xiaopeng. Background Aided Surveillance-oriented Distributed Video Coding. Picture Coding Symposium, PCS2010, Nagoya, Japan,2010:222-225.
[168]Zhang Yongbing, Zhao Debin, Liu Hongbin, et al. Side information generation with auto regressive model for low-delay distributed video coding. Journal of Visual Communication and Image Representation,23(1), Jan.2012: 229-236.
[169]Antonio Tome and Fernando Pereira. Low delay distributed video coding with refined side information. Signal Processing:Image Communication,26(4), Apr.2011:220-235.
[170]Stefaan Mys, Jurgen Slowack, Jozef Skorupa, et al. Decoder-driven mode decision in a block-based distributed video codec. Multimedia Tools and Applications,58(1), May 2012:239-266.
[171]GKarlsson. Asynchronous transfer of video. IEEE Communications Magazine,34(8), Aug.1996:118-126.
[172]M. Tagliasacchi, S.Tubaro and A. Sarti. On the modeling of motion in Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2006, Oct.2006:593-596.
[173]S. Borchert, R. P. Westerlaken, R. K. Gunnewiek, et al. On extrapolating side information in distributed video coding. Picture Coding Symposium, PCS2007, Nov.2007.
[174]S. Borchert, R. P. Westerlaken, R. K. Gunnewiek, et al. Motion compensated prediction in transform domain distributed video coding. IEEE Workshop on Multimedia Signal Processing, Oct.2008:332-336.
[175]Xianming Liu, Denting Zhai, Debin Zhao, et al. Side information extrapolation with temporal and spatial consistency. IEEE International Symposium on Circuits and Systems,2011:2918-2921.
[176]Bruee D. Lucas and Takeo Kanade. An iterative image registration teehnique with an application to stereo vision. Lnternational Joint Conferene on Artificial Intelligence, Apr.1981:674-679.
[177]S. Baker and I. Matthews. Lucas-Kanade 20years on:a unifying framework. International Journal of Computer Vision,56(3),2004:221-255.
[178]J. Y. Bouguet. Pyramidal implementation of the Lucas Kanade feature tracker: description of the algorithm. Intel Corporation Microprocessor Research Labs, 2000, http://robots.standford.edu/cs223b04/algo_tracking.pdf.
[2]ITU-T and ISO/IEC JTC1. Advanced Video Coding for Generic Audiovisual Services. ITU-T Recommendation H.264-ISO/IEC 14496-10 AVC,2005.
[3]ITU-T and ISO/IEC JTC1. Advanced video coding for generic audiovisual services Amendment 1:Support of additional colour spaces and removal of the High 4:4:4 Profile. ITU-T Recommendation H.264(2005)-Amendment 1,2005.
[4]Joint Video Team (JVT) of ISO/IEC MPEG and ITU-T VCEG. Joint Draft ITU-T Rec. H.264| ISO/IEC 14496-10/Amd.3 Scalable video coding. JVT-X201, July, 2007.
[5]Joint Collaborative Team-Video Coding. Test Model under Consideration. JCTVC-A205. Dresden, Germany, April,2010:15-23. [Online]. http://wftp3.itu.int/av-arch/jctvc-site/2010_04_A_Dresden/JCTVC-A205.zip
[6]Joint Collaborative Team-Video Coding. [Online]. http://phenix.int-evry.fr/jct/index.php
[7]D. Marpe, T. Wiegand, and G. J. Sullivan. The H.264/MPEG4 advanced video coding standard and its applications. IEEE Communications Magazine,44(8), 2006:134-143.
[8]F. Pereira, L. Torres, C. Gullemot, et al. Distributed video coding:selecting the most promising application scenarios. Elsevier Journal Signal Processing:Image Communication,23(1),2008:339-352.
[9]宗晓飞.信源网络联合编码关键技术研究及应用[学位论文],北京,北京邮电大学,2009.
[10]ISO/IEC. Information technology-coding of moving pictures and associated audio for digital storage media at up to about 1.5Mb/s, Part 1:system, Part 2: video, Part 3:audio, IS 11172,1993.
[11]TU-T and ISO/IEC. Generic coding of moving pictures and associated audio information-Part 2:video, ITU-T Rec. H.262 and ISO/IEC 13818-2 (MPEG-2 Video),1994.
[12]ITU-T. Video coding for low bitrate communication, Rec. H.263.1995.
[13]ITU-T. Video coding for low bitrate communication, Rec. H.263 Version 2, 1998.
[14]ITU-T, Enhanced reference picture selection mode, Rec. H.263 Annex U,2000.
[15]ISO/IEC. Generic coding of audio-visual objects-part 2:visual,14496-2,1999.
[16]ITU-T, Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services, Rec. H.264 and ISO/IEC 14496-10 Version 1, May 2003.
[17]ITU-T. Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services. Rec. H.264 and ISO/IEC 14496-10 Version 3, Mar.2005.
[18]ITU-T. Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services. Rec. H.264 and ISO/IEC 14496-10 Version 8, Nov.2007.
[19]ITU-T. Recommendation H.264 Advanced Video Coding for Generic Audiovisual Services. Rec. H.264 and ISO/IEC 14496-10 Version 11, Mar.2009.
[20]David R, Bull. Edward J. Delp, and Seishi Takamura. Introduction to the issue on emerging technologies for video compression. IEEE Journal of Selected Topics Signal Processing,5(7), Nov.2011:1277-1281.
[21]Joint Collaborative Team. Video Coding. Geneva, Switzerland, May,2012. [Online]. http://phenix.int-evry.fr/jct/index.php
[22]http://www.cnblogs.com/OMG-Team/archive/2011/11/03/2234917.html
[23]常侃.H.264的关键技术研究[学位论文],北京,北京邮电大学,2009.
[24]毕厚杰.新一代视频压缩编码标准——H.264/AVC,北京,人民邮电出版社,ISBN 7-115-13064-7,2009.
[25]韩钰.基于纹理合成的图像编码算法[学位论文].北京,北京邮电大学,2010.
[26]J. H. Min and Samsung. Unification of the directional intra prediction methods in TMuC. JCTVC-B100, July,2010.
[27]K. McCann, W. J. Han,I. K. Kim, et al. Samsung's Response to the call for proposals on video compression technology. JCTVC-A124, Apr.,2010.
[28]K. Ugur. K. R. Andersson. and A. Fuldseth. Description of video coding technology proposal by Tandberg. Nokia, Ericsson, JCTVC-A119, Apr.,2010.
[29]JCT-VC. High efficiency video coding (HEVC) text specification draft 6. JCTVC-H1003, Feb.,2012.
[30]K. Mccann, S. Sekiguci, B. Bross. et al. HEVC test model 3 (HM3) encoder description, JCTVC-E602,2011.
[31]T. Tan, W. Han, B. Bross, et al. Bog report of CE9:motion vector coding, JCTVC-D441,2011.
[32]Y. Huang, B. Bross, M. Zhou, et al. CE9:summary report of core experiment on MV coding and skip/merge operations, JCTVC-F029,2011.
[33]T. Wiegand, B. Bross, W. Han, et al. WD3:working draft 3 of high-efficiency video coding, JCTVC-E603,2011.
[34]S. Chong, M. Karczewicz, C. Chen, et al. CE8 subtest2:block based adaptive loop filter, JCTVC-E323,2011.
[35]C. Chen, C. Fu, C.Tsai, et al. CE8 subtest2:adaptation between pixel-based and region-based filter selection, JCTVC-E046,2011.
[36]R. Cohen, A. Vetro. CE7:Cross-verification of Samsung's (JCTVC-D357) Fast Rotational Transform. JCTVC-D030, Daegu, Korea, Oct.2011.
[37]D. Marpe, H. Schwarz, and T. Wiegand. Entropy coding in video compression using probability interval partitioning. Picture Coding Symposium, PCS 2010, Japan, Dec.2010:66-69.
[38]M. Budagavi and M. Demircin. Parallel context processing techniques for high coding efficiency entropy coding in HEVC, JCTVC-B088,2010.
[39]D. Slepian and J. Wolf. Noiseless coding of correlated information sources. IEEE Transactions on Information Theory,19(4), July,1973:471-480.
[40]A. Wyner and J. Ziv. The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory,22(1), Jan., 1976:1-10.
[41]A. Wyner. Recent results in the Shannon theory. IEEE Transactions on Information Theory,20(1), Jan.,1974:2-10.
[42]S. S. Pradhan and K. Ramchandran. Distributed source coding using syndromes (DISCUS):Design and construction. Data Compression Conference, DCC1999, Snowbird, UT. Mar.1999:158-167.
[43]X. Wang and M. Orchard. Design of trellis codes for source coding with side information at the decoder. Data Compression Conference, DCC2001, Snowbird. UT. Mar.2001:361-370.
[44]A. Aaron. Distributed source coding. [online]. http://ivms.stanford.edu/%7Eamaaaron/dsc/index.htm
[45]C. Berrou, A. Glavieux, and P. Thitimajshima. Near shannon limit error correcting coding and decoding:turbo-codes. IEEE International Conference on Communication, ICC 1993, Geneva, Switzerland, May 1993:1064-1070.
[46]A. Aaron and B. Girod. Compression with side information using turbo codes. Data Compresssion Conference, DCC2002. Snowbird, UT, Apr.,2002:252-261.
[47]D. Varodayan, A. Aaron, and B. Girod. Rate-adaptive codes for distributed source coding. EURASIP Signal Processing Journal. Special Section on Distributed Source Coding,86(11), Nov.2006:3123-3130.
[48]Q. Xu, V. Stankovic, and Z. Xiong. Distributed joint source-channel coding of video using raptor codes. IEEE Journal on Selected Areas in Communications, 25(4), May 2007:851-861.
[49]R. Zamir. The rate loss in the Wyner-ziv problem. IEEE Transactions on Information Theory,42(6), Nov.,1996:2073-2084.
[50]S. S. Pradhan, J. Chou, and K. Ramchandran. Duality between source coding and channel coding and its extension to the side information case. IEEE Transactions on Information Theory,49(5), May,2003:1181-1203.
[51]M. Fleming, Q. Zhao, and M. Effros. Network vector quantization. IEEE Trans. Inf. Theory,50(8), Aug.2004:1584-1604.
[52]M. Fleming and M. Effros. Network vector quantization. Data Compression Conference, DCC2001,2001:13-22.
[53]D. Muresan and M. Effros. Quantization as histogram segmentation:globally optimal scalar quantizer design in network systems. Data Compression Conference, DCC2002,2002:302-311.
[54]D. Rebollo-Monedero, R. Zhang, and B. Gird. Design of optimal quantizers for distributed source coding. Data Compression Conference, DCC2003, Snowbird, UT, Mar.2003:13-22.
[55]D. Rebollo-Monedero, A. Aaron, and B. Gird. Transforms for high-rate distributed source coding. The Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA,2003:850-854.
[56]A. Aaron, S. Rane, E. Setton, et al. Transform-domain Wyner-Ziv codec for video. SPIE Conference on Visual Communications and Image Processing,2004: 520-528.
[57]X. Guo, Y. Lu, F. Wu, et al. Wyner-Ziv video coding based on set partitioning in hierarchical tree. Proc. IEEE International Conference on Image Processing, ICIP2006,2006:601-604.
[58]M. Gastpar, P. Dragotti, and M. Vetterli. The distributed Karhunen-Loeve transform. IEEE Transactions on Information Theory,52(12),2006:5177-5196.
[59]A. Aaron, R. Zhang, and B. Girod. Wyner-Ziv coding of motion video, Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Nov.2002:240-244.
[60]B. Girod, A. Aaron, S. Rane, et al. Distributed video coding. Proceedings of the IEEE,93(1), Jan.2005:71-83.
[61]R. Puri and K. Ramchandran. PRISM:a new robust video coding architecture based on distributed compression principles. Annual Allerton Conference on Communication, Control and Computing, IL, Allerton, Oct.2002.
[62]R. Puri, A. Majumdar, and K. Ramchandran. PRISM:a video coding paradigm with motion estimation at the decoder. IEEE Transactions on Image Processing, 16(10), Oct.2007:2436-2448.
[63]X. Artigas, J. Ascenso, M. Dalai, et al. The DISCOVER Codec:architecture, techniques and evaluation. Picture Coding Symposium, PCS2007, Lisbon, Portugal, Nov.2007.
[64]J. Ascenso, C. Brites, and F. Pereira. Design and performance of a novel low density parity check code for distributed video coding. IEEE International Conference on Image Processing, ICIP2008, San Diego, CA, Oct. 2008:1116-1119.
[65]J. Ascenso, C. Brites, and F. Pereira. A denoising approach for iterative side information creation in distributed video coding. IEEE International Conference on Image Processing, ICIP2011, Brussels, Belgium,2011:3513-3516.
[66]A. Abou-Elailah, J. Farah, M. Cagnazzo, et al. Improved side information generation for distributed video coding. European Workshop on Visual Information Processing,2011:42-49.
[67]J. Ascenso, C. Brites, and F. Pereira. A flexible side information generation framework for distributed video coding. Multimedia Tools and Applications, 48(3), Jul.2010:381-409.
[68]Song Juan, Wang Keyan, Liu Haiying, et al. Progressive correlation noise refinement for transform domain Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2011, Brussels, Belgium,2011: 2625-2628.
[69]N. Deligiannis, J. Barbarien, M. Jacobs, et al. Side-Information-Dependent correlation channel estimation in hash-based distributed video coding. IEEE Transactions on Image Processing,21(4),2012:1934-1949.
[70]Liu Hongbin, Li Yongpeng, Liu Xianming, et al. Two-pass reconstruction in distributed video coding. Picture Coding Symposium, PCS2009,2009:1-4.
[71]Zhang Yongsheng, Xiong Hongkai, He Zhihai, et al. Reconstruction for distributed video coding:a context-adaptive markov random field approach. IEEE Transactions on Circuits and Systems for Video Technology,21(8),2011: 1100-1114.
[72]M.Tagliasacchi, J. Pedro, F. Pereira, et al. An efficient request stopping method at the turbo decoder in distributed video coding. European Signal Processing Conference, Poznan, Poland, Sept.2007.
[73]Maugey T., Yaacoub C., Farah J., et al. Side information enhancement using an adaptive hash-based genetic algorithm in a Wyner-Ziv context.2010 IEEE International Workshop on ultimedia Signal Processing, MMSP2010,2010: 298-302.
[74]Li Zhihong, Wng Anhong, and Wang Haifang. Distributed video coding based on conditional entropy hash. International Conference on Computational Aspects of Social Networks.2010:382-385.
[75]T. Sheng, G. Hua, H. Guo, et al. Rate alloeation for transform domain Wyner-Ziv video coding without feedback. ACM International Conference on Multimedia. Canada,2008,701-704.
[76]Ye Feng, Men Aidong. Xiao He, et al. Feedback-free dietributed video coding using parallelized design. Picture Coding Symposium, PCS2012. May 2012.
[77]H. Wang, N. Cheung, and A. Ortega. A framework for adaptive scalable video coding using Wyner-Ziv techniques. EURASIP Journal on Applied Signal Processing,2006:1-18.
[78]Q. Xu and Z. Xiong. Layered Wyner-Ziv Video Coding. IEEE Trans. on Image Processing.15(12), Dec.2006:3791-3803.
[79]J. Q. Pedro, L. D. Soares, C. Brites, et al. Studying error resilience performance for a feedback channel based transform domain Wyner-Ziv video codec. Picture Coding Symposium, PCS2007, Lisbon, Portugal, Nov.2007.
[80]Zhang Yongsheng, Xiong Hongkai, He Zhihai, et al. An error resilient video coding scheme using embedded Wyner-Ziv description with decoder side non-stationary distortion modeling. IEEE Transactions on Circuits and Systems for Video Technology,21(4),2011:498-512.
[81]J. Ascenso, C. Brites, and F. Pereira. Improving frame interpolation with spatial motion smoothing for pixel domain distributed video coding. EURASIP Conference on Speech and Image Processing, Multimedia Communications and Services, Smolenice, Slovak Republic, July 2005.
[82]L. Natario, C. Brites, J. Ascenso, et al. Extrapolating side information for low-delay pixel-domain distributed video coding. International Workshop on Very Low Bit rate Video Coding, Sardinia, Italy, Sept.2005:16-21.
[83]A. Aaron, S. Rane, B. Girod. Wyner-Ziv video coding with hash-based motion compensation at the receiver. IEEE International Conference on Image Processing, ICIP2004, Singapore, Oct.2004:3097-3100.
[84]N. Deligiannis, M. Jacobs, F. Verbist, et al. Efficient hash-driven Wyner-Ziv video coding for visual sensors. IEEE International Conference on Distributed Smart Cameras, ICDSC2011,2011,1-6.
[85]S. Klomp, Y. Vatis, and J. Ostermann. Side information interpolation with sub-pel motion compensation for Wyner-Ziv decoder. International Conference on Signal Processing and Multimedia Applications, Setubal, Portugal, Aug., 2006.
[86]Liu Rongke, Yue Zhia, and Chen Changwen. Side information generation based on hierarchical motion estimation in distributed video coding. Chinese Journal of Aeronautics,22(2),2009:167-173.
[87]X. Artigas and L. Torres. Iterative generation of motion-compensated side information for distributed video coding. IEEE International Conference on Image Processing, ICIP2005, Genova, Italy, Sept.2005:833-836.
[88]M. O. Akinola, L. S. Dooley, and P. K. C. Wong. Wyner-Ziv side information generation using a higher order piecewise trajectory temporal interpolation algorithm. International Conference on Graphic and Image Processing, Manila, Philippines, Dec.2010.
[89]M. O. Akinola, L. S. Dooley, P. K. C. Wong. Improved side information generation using adaptive overlapped block motion compensation and higher-order interpolation. International Conference on Systems. Signals and Image Processing,2011:1-4.
[90]C. Yaacoub, J. Farah, and B. Pesquet-Popescu. Agenetic algorithm for side information enhancement in distributed video coding. IEEE International Conference on Image Processing, ICIP2009, Cairo, Egypt, Nov.2009: 2933-2936.
[91]X. Fan, O. Au, N. Cheung, et al. Successive refinement based Wyner-Ziv video compression. Signal Processing:Image Communication., vol.25, Jan.2010: 47-63.
[92]P. Meyer, R. Westerlaken, R. Gunnewiek, et al. Distributed source coding of video with non-stationary side information. SPIE Conference on Visual Communications and Image Processing,5960,2005:857-866.
[93]M. Dalai, R. Leonardi and F. Pereira. Improving turbo codec integration in pixel-domain distributed video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2006,2006.
[94]C. Brites and F. Pereira. Correlation noise modeling for efficient pixel and transform domain Wyner-Ziv video coding. IEEE Transactions on Circuits and Systems for Video Technology,18(9),2008:1177-1190.
[95]X. Fan, O. Au, and N. Cheung. Adaptive correlation estimation for general Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2009,2009:1409-1412.
[96]N. Deligiannis, A. Munteanu, T. Clerckx, et al. Modeling the correlation noise in spatial domain distributed video coding. Data Compression Conference, DCC2009,2009:443.
[97]L. Stankovic, V. Stankovic, S. Wang, et al. Distributed video coding with particle filtering for correlation tracking. European Signal Processing Conference, Aug.2010.
[98]Wang Shuang, Cui Lijuan, Lina Stankovic, et al. Adaptive correlation estimation with particle filtering for distributed video coding. IEEE Transactions on Circuits and Systems for Video Technology, PP(99),2011:1-10.
[99]C. Brites, J. Ascenso, J. Q. Pedro, et al. Evaluating a feedback channel based transform domain Wyner-Ziv video codec. Signal Processing:Image Communucation,23(4), Apr.2008:269-297.
[100]D. Kubasov, K. Lajnef, and C. Guillemot. A hybrid encoder/decoder rate control for Wyner-Ziv video coding with a feedback channel. IEEE Workshop on Multimedia Signal Processing, MMSP2007. Chania, Crete, Greece, Oct.2007.
[101]J. D. Areia. J. Ascenso, C. Brites, et al. Low complexity hybrid rate control for lower complexity Wyner-Ziv video decoding. European Signal Proceeding Conference, Lausanne, Switzerland, Aug.2008.
[102]X. Artigas and L. Torres. Improved signal reconstruction and return channel suppression in distributed video coding systems. ELMAR, Zadar, Croatia, Jun. 2005:53-56.
[103]M. Morbee, J. Prades-Nebot, A. Pizurica, et al. Rate allocation algorithm for pixel-domain distributed video coding without feedback channel. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP207, Honolulu, HI, USA, Apr.2007.
[104]M. Morbee, J. Prades-Nebot, A. Roca, et al. Improved pixel-based rate allocation for pixel-domain distributed video coders without feedback channel. chapter in Lecture Notes in Computer Science. Advanced Concepts for Intelligent Vision Systems, Vol.4678, Aug.2007:663-674.
[105]J. L. Martinez, G. Fernandez-Escribano, H. Kalva, et al. Feedback free DVC architecture using machine learning. IEEE International Conference on Image Processing, ICIP2008, San Diego, CA, USA, Oct.2008:1140-1143.
[106]T. Sheng, X. Zhu, G. Hua, et al. Feedback-free rate-allocation scheme for transform domain Wyner-Ziv video coding. Multimedia Systems,16(2),2010: 127-137.
[107]C. Brites and Pereira F. An efficient encoder rate control solution for transform domain Wyner-Ziv video coding. IEEE Transactions on Circuits and Systems for Video Technology,21(9),2011:1278-1292.
[108]P. Ishwar, V. M. Prabhakaran, and K. Ramchandran. Towards a theory for video coding using distributed compression principles. IEEE International Conference on Image Processing, ICIP2003, Barcelona, Catalonia, Spain, Sept. 2003:687-690.
[109]G. R. Esmaili and P. Cosman, Correlation noise classification based on matching success for transform domain Wyner-Ziv video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2009, Apr.2009:801-804.
[110]R. Westerlaken, S. Borchert, R. Gunnewiek, et al. Dependency channel modeling for a ldpc-based wyner-ziv video compression scheme. IEEE International Conference on Image Processing, ICIP2006,2006:277-280.
[111]M. Cagnazzo, T. Maugey, and B. Pesquet-Popescu. A differential motion estimation method for image interpolation in distributed video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2009, Taipei Taiwan,2009:1861-1864.
[112]M. Cagnazzo, W. Miled, T. Maugey, et al. Image interpolation with edge-preserving differential motion refinement. IEEE International Conference on Image Processing, ICIP2009,2009:361-364.
[113]G. Petrazzuoli, M. Cagnazzo and B. Pesquet-Popescu. High order motion interpolation for side information improvement in DVC. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2010, Dallas Texas USA,2010:2342-2345.
[114]J. Ascenso, C. Brites, and F. Pereira. Motion compensated refinement for low complexity pixel based distributed video coding. IEEE International Conference on Advanced Video and Signal Based Surveillance, Como, Italy, Sept.2005.
[115]D. Varodayan, D. Chen, M. Flierl, et al. Wyner-Ziv coding of video with unsupervised motion vector learning. Signal Processing:Image Communication. 23(5), June 2008:369-378.
[116]I.H. Tseng and A. Ortega. Motion estimation at the decoder using maximum likelihood techniques for distributed video coding. Asilomar Conference on Signals. Systems and Computers, Pacific Grove, CA, USA, Nov.2005:756-760.
[117]Greg Welch and Gary Bishop. An introduction to the Kalman Filter. [online]. NC 2006, Chapel Hill. www.cs.unc.edu/-welch/kalman/kalmanlntro.html.
[118]C.M. Kuo, C.H. Hsieh, Y.D. Jou, et al. Motion estimation for video compression using Kalman filtering. IEEE Transactions on Broadcasting,42, 1996:110-116.
[119]Yi Luo and Mehmet Celenk. Motion estimation for video compression using Kalman filtering with adaptive adjustment. The International Conference on Visual Information Engineering,2008:612-617.
[120]J. E. Fowler, M. Tagliasacchi and B. pesquet Popescu. Wavelet based distributed source coding of video. [online]. http://signal.ee.bilk.ent.edu.tr/def-eyent/papers/crl535.pdf,2005.
[121]Wang Anhong, Zhao Yao, and Li Jing. Wavelet-Domain distributed video coding with motion-compensated refinement. IEEE International Conference on Image Processing, ICIP2006. Atlanta GA USA, Oct.2006:241-244.
[122]Guo Xun, Lu Yan, Wu Feng, et al. Distributed video coding using wavelet. IEEE International Symposium on Circuits and Systems,2006:5427-5430.
[123]Liu W., Dong L., and Zeng W. Estimating side-information for Wyner-Ziv video coding using resolution-progressive decoding and extensive motion exploration. IEEE International Conference on Acoustics. Speech, and Signal Processing, ICASSP2009, Taipei Taiwan, Apr.2009:721-724.
[124]R. Bernardini, R. Rinaldo, A. Vitali. et al. Performance evaluation of wavelet-based distributed video coding schemes, Signal, Image and Video Processing,5(1),2011:49-60.
[125]Li Xin. On the importance of source classification in Wyner-Ziv video coding. SPIE Conference on Visual Communicatio and Image Processig, San Jose, California, USA, Jan.2008.
[126]Zhang Jinrong, Li Houqiang, Liu Qiwei, et al. A transform domain classification based wyner-ziv video coding. International Conference on Multimedia and Expo, ICME2007,2007:144-147.
[127]R. Bernardini, R. Rinaldo, P. Zontone, et al. Wavelet domain distributed coding for video. IEEE International Conference on Image Processing, ICIP2006, Atlanta GA USA, Oct.2006:245-248.
[128]王安红.分布式视频编码研究[学位论文].北京,北京交通大学,2009.
[129]J. Oliver and M. P. Malumbres. Low-complexity multiresolution image compression using wavelet lower trees. Transactions on Circuits and Systems for Video Technology,16,2006:1437-1444.
[130]侯萌洁.分布式视频编码中的信道建模及边信息改进算法研究[学位论文].北京,北京邮电大学,2011.
[131]A. D. Liveris, Z. Xiong and C. N. Geoghiades. Compression of binary sources with side information using low-density parity-check codes. Global Telecommunications Conference,2002:1300-1304.
[132]W. J. Chien and L. Karam. BLAST-DVC:BitpLAne SelecTive distributed video coding. Multimedia Tools and Applications,48(3), Apr.2010:37-456.
[133]Z. Belkoura and T. Sikora. Improving Wyner-Ziv video coding by block-based distortion estimation. European Signal Processing Conference,2006.
[134]Feng Ying. Li Yunsong, Wu Chengke, et al. Coding scheme with skip mode based on motion filed detection for DVC. Processings of SPIE-the International Society for Opitical Engineering,2008.
[135]S. Mys, J. Slowack, J. Skorupa, et al. Introducing skip mode in distributed video coding. Signal Processing:Image Communication,24(3),2009:200-213.
[136]W. J. Chien, L. Karam, and G. Abousleman Block-adaptive wyner-ziv coding for transform-domain distributed video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2007,2007.
[137]T. Do, H. J. Shim and B. Jeon. Motion linearity based skip decision for Wyner-Ziv coding. International Conference on Computer Science and Information Technology,2009
[138]A. Trapanese, M. Tagliasacchi, S. Tubaro, et al. Embedding a block-based intra mode in frame-based pixel domain Wyner-Ziv video coding. International Workshop on Very Low Bitrate Video,2005.
[139]M. Tagliasacchi, A. Trapanese, S. Tubaro, et al. Intra mode decision based on spatio-temporal cues in pixel domain Wyner-Ziv video coding. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2006,2006.
[140]C. TsaiD, M. LeeC, and N. LieW. Dynamic key block decision with spatio-temporal analysis for Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2007,2007.
[141]J. Ascenso and F. Pereira. Low complexity intra mode selection for efficient distributed video coding. International Conference on Multimedia and Expo, ICME2009,2009:101-104.
[142]G. Esmaili and P. Cosman. Low complexity spatio-temporal key frame encoding for Wyner-Ziv video coding. Data Compression Conference, DCC2009, 2009.
[143]S. Benierbah and M. Khamadja. Hybrid Wyner-Ziv and intra video coding with partial matching motion estimation at the decoder. IEEE International Conference on Image Processing, ICIP2009,2009:2925-2928.
[144]A. Tekalp. Digital Video Processing. Prentice-Hall, Englewood Cliffs, NJ, 1995.
[145]T. Koga, K. Iinuma, A. Hirano, et al. Motion compensated interframe coding for video conferencing. Proceedings of the National Telecommunications Conference.1981:C9.6.1-C9.6.5.
[146]L. Po and W. Ma. A novel four-step search algorithm for fast block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology,6 (3).1996:313-317.
[147]R. Li, B. Zeng, and M. Liou. A new three-step search algorithm for block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology,4 (4),1994:438-442.
[148]L. Liu, E. Feig. A block-based gradient descent search algorithm for block motion estimation in video coding. IEEE Transactions on Circuits and Systems for Video Technology,6(4),1996:419-422.
[149]C. Cheung and L. Po. A novel cross-diamond search algorithm for fast block motion estimation. IEEE Transactions on Circuits and Systems for Video Technology,12 (12),2002:1168-1177.
[150]C. Cheung and L. Po. A novel small-cross-diamond search algorithm for fast video coding and video conferencing applications. IEEE International Conference on Image Processing, ICIP2002, Sept.2002:681-684.
[151]W. J. Chien, J. K. Lina, and P. A. Glen. Distributed video coding with 3-D recursive search block matching. IEEE International Symposium on Circuits and Systems, Puerto Rico, August 2006.
[152]Y. K. Wang, M. M. Hannuksela, V. Varsa, et al. The error concealment feature in the H.26L test model. IEEE International Conference on Image Processing, ICIP2002, Sept.2002:729-736.
[153]D. Agrafiotis, D. R. Bull, and C. N. Canagarajah. Enhanced error concealment with mode selection. IEEE Transactions on Circuits and Systems for Video Technology,16(8), Aug.2006:960-973.
[154]W. Zhu, Y. Wang and Q. F. Zhu. Second-order derivative- based smoothness measure for error concealment in DCT-based codecs. IEEE Transactions on Circuits and Systems for Video Technology,8(6),1998:713-718.
[155]S. Ye, X. Lin and Q. Sun. Content based error detection and concealment for image transmission over wireless channel. IEEE International Symposium on Circuits and Systems, vol.2, Bangkok, Thailand, May 2003:368-371.
[156]J. H. Zheng and L. P. Chau. A temporal error concealment algorithm for H.264 using Lagrange interpolation. IEEE International Symposium on Circuits and Systems,2004:133-136.
[157]W. N. Lie and Z. W. Gao. Video error concealment by intergrating greedy suboptimization and Kalman filtering techniques. IEEE Transactions on Circuits and Systems for Video Technology,16,2006:982-992.
[158]Y. Chen, X. Sun, F. Wu, et al. Spatio-temporal video error concealment using priority-ranked region-matching. IEEE International Conference on Image Processing, ICIP2005,2005:1050-1053.
[159]L. Atzori, N. De, F. G. B., et al. A spatio-temporal concealment technique using boundary matching algorithm and mesh-based warping (BMA-MBW). IEEE Transactions on Multimedia,3(3), Sep.2001:326-338.
[160]Hongbin Liu, Xiangyang Ji, Debin Zhao, et al. Distributed Video Coding using block based checkerboard pattern splitting algorithm. Picture Coding Symposium, PCS2007, Lisbon, Portugal, Nov.2007.
[161]A.Hampapur, L.Brown, J.Connell, et al. Smart video surveillance:Exploring the concept of multiscale spatio temporal tracking. IEEE Signal Processing Magazine,22(2), Mar.2005:38-51.
[162]W. Hu, T. Tan, L.Wang, et al. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C:Applications and Reviews,34(3), Aug.2004:334-352.
[163]R.Kumar, H.Sawhney, S. Samarasekera, et al. Aerial video surveillance and exploitation. IEEE Processing,89(10), Oct.2001:1518-1539.
[164]T. Wiegand, G. J. Sullivan, G. Bjntegaard, et al. Overview of the H.264/AVC video coding standard. IEEE Transactions on Circuits and Systems for Video Technology,13(7), Jul.2003:560-576.
[165]P. Kumar, A. Pande, and A. Mittal. Efficient compression and network adaptive video coding for distributed video surveillance. Multimedia Tools and Applications,56, Dec.2010:365-384.
[166]Liu Limin, Li Zhen and E. J. Delp. Efficient and Low-Complexity Surveillance Video Compression Using Backward-Channel Aware Wyner-Ziv Video Coding. IEEE Transactions on Circuits and Systems for Video Technology, 19(4), Apr.2009:453-465.
[167]Liu Hongbin, Ma Siwei and Fan Xiaopeng. Background Aided Surveillance-oriented Distributed Video Coding. Picture Coding Symposium, PCS2010, Nagoya, Japan,2010:222-225.
[168]Zhang Yongbing, Zhao Debin, Liu Hongbin, et al. Side information generation with auto regressive model for low-delay distributed video coding. Journal of Visual Communication and Image Representation,23(1), Jan.2012: 229-236.
[169]Antonio Tome and Fernando Pereira. Low delay distributed video coding with refined side information. Signal Processing:Image Communication,26(4), Apr.2011:220-235.
[170]Stefaan Mys, Jurgen Slowack, Jozef Skorupa, et al. Decoder-driven mode decision in a block-based distributed video codec. Multimedia Tools and Applications,58(1), May 2012:239-266.
[171]GKarlsson. Asynchronous transfer of video. IEEE Communications Magazine,34(8), Aug.1996:118-126.
[172]M. Tagliasacchi, S.Tubaro and A. Sarti. On the modeling of motion in Wyner-Ziv video coding. IEEE International Conference on Image Processing, ICIP2006, Oct.2006:593-596.
[173]S. Borchert, R. P. Westerlaken, R. K. Gunnewiek, et al. On extrapolating side information in distributed video coding. Picture Coding Symposium, PCS2007, Nov.2007.
[174]S. Borchert, R. P. Westerlaken, R. K. Gunnewiek, et al. Motion compensated prediction in transform domain distributed video coding. IEEE Workshop on Multimedia Signal Processing, Oct.2008:332-336.
[175]Xianming Liu, Denting Zhai, Debin Zhao, et al. Side information extrapolation with temporal and spatial consistency. IEEE International Symposium on Circuits and Systems,2011:2918-2921.
[176]Bruee D. Lucas and Takeo Kanade. An iterative image registration teehnique with an application to stereo vision. Lnternational Joint Conferene on Artificial Intelligence, Apr.1981:674-679.
[177]S. Baker and I. Matthews. Lucas-Kanade 20years on:a unifying framework. International Journal of Computer Vision,56(3),2004:221-255.
[178]J. Y. Bouguet. Pyramidal implementation of the Lucas Kanade feature tracker: description of the algorithm. Intel Corporation Microprocessor Research Labs, 2000, http://robots.standford.edu/cs223b04/algo_tracking.pdf.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |