能量受限条件下的手语视频编码方法研究
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摘要
手语是由手形、手臂运动并辅之以表情、唇动以及其他体势表达思想的视觉语言,是聋哑人进行交流的最自然方式。与头肩视频不同,手语视频由于增加了手形、手臂运动,并且存在手脸遮挡现象,所以更为复杂,对其进行研究难度更大。和手语视频识别与合成研究相比,目前针对手语视频的编码研究还较少,且大多数都是基于率失真(Rate-Distortion, R-D)理论,以给定编码码率为约束,研究编码码率和失真之间的关系,使重建手语视频的失真最小。但是,随着无线网络带宽的快速增加和新一代视频编码标准H.264的广泛应用,编码码率的约束性已经越来越弱,而无线视频终端在功耗上所受的制约却越来越强。因此,如何在无线视频终端能量有限的约束条件下,使手语视频经编码后的失真最小,减小能耗、延长电池的更新周期已成为一个迫切需要解决的问题。
本论文对能量受限条件下的手语视频编码进行了深入的研究,目的是利用聋哑人视觉选择注意机制、功率率失真理论和感兴趣区能量分配视频编码方法实现手语视频编码功耗、编码码率和编码失真之间的动态平衡优化,在确保手语视频主客观编码质量的同时,尽可能降低无线视频终端总体功耗,延长电池更新周期,为解决能量受限条件下聋哑人手语视频编码的最优化参数配置和资源分配提供新理论和新方法。本论文的研究工作主要包括：
(1)理论分析和实验统计了影响H.264手语视频编码复杂度的因素,将H.264手语视频编码器参数按照复杂度分为四种不同的级别,每种级别具有不同的编码复杂度和编码质量,然后依据无线视频终端电池能量和视频运动复杂性自适应地选择编码级别。实验结果表明该方法在保证手语视频编码质量基本不变的同时,能够减少编码器计算复杂度,节省无线视频终端系统的计算资源。
(2)综合考虑无线视频终端电池能量的时变性和聋哑人视觉注意机制的不平衡性,建立了感兴趣区能量感知手语视频编码方法,该方法在帧层依据无线视频终端当前可使用电池能量和视频帧复杂度确定参考帧数和搜素范围,在宏块层依据手语视频不同宏块区域的视觉重要性确定宏块预测模式和量化系数,最后根据帧层和宏块层共同确定的参数进行编码。实验结果表明该方法在保证手语视频感兴趣区编码质量的同时,能够进一步减少编码器计算复杂度,节省无线视频终端系统的计算资源。
(3)详细分析了H.264帧内、帧间和跳帧三种编码模式的功率率失真(Power-Rate-Distortion,P-R-D)特性,在此基础上,分别建立了编码一帧手语视频的能耗模型和P-R-D模型,并提出了优化一帧视频中采用帧内、帧间和跳帧编码模式宏块个数的算法,实验表明所提出的P-R-D模型和实测P-R-D性能相吻合。
(4)针对手脸遮挡条件下的手语视频手势检测问题,提出一种基于力场(Force Field)转换的手势检测方法。该方法首先分别计算手脸遮挡帧和纯脸部帧的力场图像,然后将力场图像分块并统计各分块直方图特征,再将相同空间位置的分块直方图对应相减,得到各分块直方图灰度分量差,最后将各分块直方图灰度分量差与灰度阈值进行比较获得手部位置。实验证明该方法能够实时进行手脸遮挡条件下的手势检测。
Sign language is a highly structured, linguistically complete, natural language system that expresses vocabulary and grammar visually and spatially using a complex combination of facial expressions (such as eyebrow movements, eye blinks and mouth/lip shapes), hand gestures, body movements and finger-spelling that change in space and time. Compared with head and shoulder video, sign language video is more complex and the reaseach about it is challenging.Currently, the reaseaches about sign language video encoding are limited and mostly based on Rate-Distortion theory to achieve the minimum distortion of decoded sign languge video. However, the R-D theory mainly research on the relationship between Rate and Distortion under the rate constraint. With the rapid development of wireless communication, the enhancement of the wireless channel bandwidth, and the popularity of Advanced Video Coding standard H.264, the constraints on the rate become weaker and weaker. At the meantime, the processing capabilities insufficiency of mobile devices and the microprocessor's power-constraint problem caused by battery power become the major restriction to the development of mobile sign language communication.
This dissertation conducts in-depth research on sign language video encoding. The work aims to achieve the optimal balance among encoding power, encoding rate and encoding distortion by utilizing the visual selection attention mechanism of deaf community, Power-Rate-Distortion theory and regions of interest power allocation method.
In general, the research of this dissertation can be summarized as follows:
(1) The factors which will affect the complexity of sign language video encoder are analyzed at first. Based on the analysis results, a novel computation resource allocation algorithm is proposed. The algorithm can allocate the computation resource of the encoder adaptive to available battery power and video contents. Experimental results show the proposed algorithm can highly reduce the computation resource while maintaining video coding quality.
(2) A scheme which allocates the computational resource of the sign language video encoder adaptive to available battery power and deaf people's visual system is proposed. In the scheme, encoding levels which determine number of reference frames and search range are adaptively selected according to the battery power and frame complexity at frame level. Then possible partition mode and quantization parameter are adaptively adjusted at the macro block (MB) level according to the relative priority of each MB. Experimental results show that the proposed algorithm obtains better peak-signal-noise-rate of face and hands that improves the intelligibility of sign language video, the computation complexity of encoder is reduced further.
(3)An analytic P-R-D model to obtain optimized tradeoffs among power consumption, bit rate, and distortion for sign language video encoding is proposed. In particular, numbers of different macroblock (MB) coding modes are intelligently controlled through an optimization process according to their distinct P-R-D performance. Both the analytic and simulation results have shown the applicability of our scheme for mobile sign language video encoding.
(4) A novel algorithm to track the hand during hand over face occlusion in sign language video is proposed. The algorithm is based on image force field transformation. First, the frames with a hand occluding the face and those with only a face are transformed to force field images. Then the force field images are partitioned into sub-images and the histograms of each sub-image are calculated. For each sub-image, the histogram of frame with only a face is subtracted from the frame with a hand occluding the face to get the difference histogram. Finally, for each sub-image the difference histogram is compared to threshold to get the position of the hand. Experimental results show that the proposed algorithm is capable of real-time tracking of hand.
本论文对能量受限条件下的手语视频编码进行了深入的研究,目的是利用聋哑人视觉选择注意机制、功率率失真理论和感兴趣区能量分配视频编码方法实现手语视频编码功耗、编码码率和编码失真之间的动态平衡优化,在确保手语视频主客观编码质量的同时,尽可能降低无线视频终端总体功耗,延长电池更新周期,为解决能量受限条件下聋哑人手语视频编码的最优化参数配置和资源分配提供新理论和新方法。本论文的研究工作主要包括：
(1)理论分析和实验统计了影响H.264手语视频编码复杂度的因素,将H.264手语视频编码器参数按照复杂度分为四种不同的级别,每种级别具有不同的编码复杂度和编码质量,然后依据无线视频终端电池能量和视频运动复杂性自适应地选择编码级别。实验结果表明该方法在保证手语视频编码质量基本不变的同时,能够减少编码器计算复杂度,节省无线视频终端系统的计算资源。
(2)综合考虑无线视频终端电池能量的时变性和聋哑人视觉注意机制的不平衡性,建立了感兴趣区能量感知手语视频编码方法,该方法在帧层依据无线视频终端当前可使用电池能量和视频帧复杂度确定参考帧数和搜素范围,在宏块层依据手语视频不同宏块区域的视觉重要性确定宏块预测模式和量化系数,最后根据帧层和宏块层共同确定的参数进行编码。实验结果表明该方法在保证手语视频感兴趣区编码质量的同时,能够进一步减少编码器计算复杂度,节省无线视频终端系统的计算资源。
(3)详细分析了H.264帧内、帧间和跳帧三种编码模式的功率率失真(Power-Rate-Distortion,P-R-D)特性,在此基础上,分别建立了编码一帧手语视频的能耗模型和P-R-D模型,并提出了优化一帧视频中采用帧内、帧间和跳帧编码模式宏块个数的算法,实验表明所提出的P-R-D模型和实测P-R-D性能相吻合。
(4)针对手脸遮挡条件下的手语视频手势检测问题,提出一种基于力场(Force Field)转换的手势检测方法。该方法首先分别计算手脸遮挡帧和纯脸部帧的力场图像,然后将力场图像分块并统计各分块直方图特征,再将相同空间位置的分块直方图对应相减,得到各分块直方图灰度分量差,最后将各分块直方图灰度分量差与灰度阈值进行比较获得手部位置。实验证明该方法能够实时进行手脸遮挡条件下的手势检测。
Sign language is a highly structured, linguistically complete, natural language system that expresses vocabulary and grammar visually and spatially using a complex combination of facial expressions (such as eyebrow movements, eye blinks and mouth/lip shapes), hand gestures, body movements and finger-spelling that change in space and time. Compared with head and shoulder video, sign language video is more complex and the reaseach about it is challenging.Currently, the reaseaches about sign language video encoding are limited and mostly based on Rate-Distortion theory to achieve the minimum distortion of decoded sign languge video. However, the R-D theory mainly research on the relationship between Rate and Distortion under the rate constraint. With the rapid development of wireless communication, the enhancement of the wireless channel bandwidth, and the popularity of Advanced Video Coding standard H.264, the constraints on the rate become weaker and weaker. At the meantime, the processing capabilities insufficiency of mobile devices and the microprocessor's power-constraint problem caused by battery power become the major restriction to the development of mobile sign language communication.
This dissertation conducts in-depth research on sign language video encoding. The work aims to achieve the optimal balance among encoding power, encoding rate and encoding distortion by utilizing the visual selection attention mechanism of deaf community, Power-Rate-Distortion theory and regions of interest power allocation method.
In general, the research of this dissertation can be summarized as follows:
(1) The factors which will affect the complexity of sign language video encoder are analyzed at first. Based on the analysis results, a novel computation resource allocation algorithm is proposed. The algorithm can allocate the computation resource of the encoder adaptive to available battery power and video contents. Experimental results show the proposed algorithm can highly reduce the computation resource while maintaining video coding quality.
(2) A scheme which allocates the computational resource of the sign language video encoder adaptive to available battery power and deaf people's visual system is proposed. In the scheme, encoding levels which determine number of reference frames and search range are adaptively selected according to the battery power and frame complexity at frame level. Then possible partition mode and quantization parameter are adaptively adjusted at the macro block (MB) level according to the relative priority of each MB. Experimental results show that the proposed algorithm obtains better peak-signal-noise-rate of face and hands that improves the intelligibility of sign language video, the computation complexity of encoder is reduced further.
(3)An analytic P-R-D model to obtain optimized tradeoffs among power consumption, bit rate, and distortion for sign language video encoding is proposed. In particular, numbers of different macroblock (MB) coding modes are intelligently controlled through an optimization process according to their distinct P-R-D performance. Both the analytic and simulation results have shown the applicability of our scheme for mobile sign language video encoding.
(4) A novel algorithm to track the hand during hand over face occlusion in sign language video is proposed. The algorithm is based on image force field transformation. First, the frames with a hand occluding the face and those with only a face are transformed to force field images. Then the force field images are partitioned into sub-images and the histograms of each sub-image are calculated. For each sub-image, the histogram of frame with only a face is subtracted from the frame with a hand occluding the face to get the difference histogram. Finally, for each sub-image the difference histogram is compared to threshold to get the position of the hand. Experimental results show that the proposed algorithm is capable of real-time tracking of hand.
引文
[1]Zhihai He, Yongfang Liang, Lulin Chen, et al. Power-Rate-Distortion Analysis for Wireless Video Communication under Energy Constraints [J].IEEE Transactions on Circuits and Systems for Video Technology, 2005,15(5):645-658
[2]Zhihai He, Sanjit K Mitra. From Rate-Distortion Analysis to Resource-Distortion Analysis [J]. IEEE Circuits and Systems Magazine, 2005,5(3):6-18
[3]Wenye Cheng, Xi Chen, Zhihai He. Doubling of the Operational Lifetime of Portable Video Communication Devices Using Power-Rate-Distortion Analysis and Control[C]. IEEE International Conference on Image Processing, October, 2006, 2473-2476
[4]Zhihai He, Wenye Cheng, Xi Chen. Energy Minimization of Portable Video Communication Devices Based on Power-Rate-Distortion Optimization [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2008,18(5):596-608
[5]Yiran Li, Guiqiang Dong, and Tong Zhang. Joint Source-Channel Coding and Channelization for Embedded Video Processing With Flash Memory Storage[J]. IEEE Transactions on Image Processing, 2012,60(8):4403-4415
[6]Younghoon Lee, Jungsoo Kim, and Chong-Min Kyung. Energy-Aware Video Encoding for Image Quality Improvement in Battery-Operated Surveillance Camera[J]. IEEE Transactions on Very Large Scale Integration Systems, 2012,20(2):310-319
[7]Junni Zou, Hongkai Xiong, Chenglin Li, et al.Lifetime and Distortion Optimization With Joint Source/Channel Rate Adaptation and Network Coding-Based Error Control in Wireless Video Sensor Networks[J]. IEEE Transactions on Vehicular Technology, 2011, 60(3):1182-1195
[8]Malisa Marijan, Ilker Demirkol, Danijel Maricic, et al. Adaptive Sensing and Optimal Power Allocation for Wireless Video Sensors With Sigma-Delta Imager[J]. IEEE Transactions on Image Processing, 2010,19(10):2540-2555
[9]Xiang Li, Mathias Wien, Jens-Rainer Ohm. Rate-Complexity-Distortion Optimization for Hybrid Video Coding[J].IEEE Transactions on Circuits and Systems for Video Technology, 2011,21(7):957-971
[10]陆寄远,张培钊,段晓华,等.一种复杂度约束下基于宏块优先顺序的运动估计优化算法[J].计算机研究与发展,2011,48(3)：494-500
[11]Xuejuan Gao,Kin Man Lam, Li Zhuo, et al. Complexity scalable control for H.264 motion estimation and mode decision under energy constraints[J].Signal Processing, 2010,90(8):2468-2479
[12]Chungjr Lian, Pochih Tseng, Lianggee Chen. Low-Power and Power-A ware Video Codec Design: An Overview [J].China Communications, 2006 October: 45-51
[13]Chungjr Lian, Shaoyi Chien, Chiaping Lin, et al. Power-Aware Multimedia:Concepts and Perspectives [J]. IEEE Circuits and Systems Magazine, 2007,7(2):26-34
[14]Yuhan Chen, Tungchien Chen, Chuanyung Tsai, et al. Algorithm and Architecture Design of Power-Oriented H.264/AVC Baseline Profile Encoder for Portable Devices [J].IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19(8): 1118-1128
[15]Wen Ji, Jiangchuan Liu, Min Chen, et al. Power-Efficient Video Encoding on Resource-Limited Systems:A Game-Theoretic Approach[J].Future Generation Computer Systems,2012,28:427-436
[16]Yang Liu, Zheng Guo Li, Yeng Chai Soh. Region-of-Interest based H.264 Encoding Parameter Allocation for Low Power Video Communication[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2008,18(1):134-139
[17]Avin Kumar Kannur, Baoxin Li. Power-A ware Content-Adaptive H.264 Video Encoding[C].IEEE International Conference on Acoustics, Speech and Signal Processing, 2009,925-928
[18]Minghui Wang, Tianruo Zhang, Chen Liu, et al. Region-of-Interest Based H.264 Encoding Parameter Allocation for Low Power Video Communication[C]. The 5th International Colloquium on Signal Processing and Its Applications, March 2009, 233-237
[19]Yayu Zheng, Fan Zhou, Xiang Tian, et al. Lightweight Content-Adaptive Coding in Joint Analyzing-Encoding Framework[J]. IEEE Transactions on Consumer Electronics, 2008, 54(2):614-622
[20]A. Cavender, R. Ladner, E. Riskin.MobileASL:Intelligibility of Sign Language Video as Constrained by Mobile Phone Technology[C]. Proceedings of ASSETS:The Eighth International ACM SIGACCESS Conference on Computers and Accessibility, October 2006:71-78
[21]R.Vanam, F.Ciaramello, E. Riskin, et al. Joint Rate-Intelligibility-Complexity Optimization of an H.264 Video Encoder for American Sign Language[C]. Western New York Image Processing Workshop (WNYIP), Rochester, NY, September 2009
[22]J. Chon, N. Cherniavsky, E. Riskin, et al.. Enabling Access through Real-Time Sign Language Communication over Cell Phones[C].43rd Annual Asilomar Conference on Signals, Systems, and Computers, November 2009:588-592
[23]K. Emmorey, R. Thompson, and R. Colvin. Eye Gaze during Comprehension of American Sign Language by Native and Beginning signers [J]. Journal of Deaf Students and Deaf Education,2009,14(2):237-243
[24]Davide Bottari, Matteo Valsecchi and Francesco Pavani. Prominent Reflexive Eye-Movement Orienting Associated with Deafness[J].Cognitive Neuroscience, 2012 3(1):8-13
[25]黎洪松.数字视频处理[M].北京邮电大学出版社,2006
[26]李斌.面向高性能视频编码标准的率失真优化技术研究[D].合肥：中国科学技术大学,2013
[27]韦耿.视频编码功率率失真模型及低复杂度算法研究[D].武汉：华中科技大学,2007
[28]C. L. James ,K. M. Reischel. Text Input for Mobile Devices:Comparing Model Prediction to Actual Performance[C]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2001:365-371
[29]Starner T,Weaver J,Pentland A. Real-time American Sign Language Recognition Using Desk and Wearable Computer Based Video[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998,20(12):1371-1375
[30]Lee HK. Kim JH. An HMM Based Threshold Model Approach for Gesture Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(10):961-973
[31]Lankton R, Fitzgibbon AW. Real-Time Gesture Recognition Using Deterministic Boosting[C]. Proceeding of British Machine Vision Conference, 2002:817-826
[32]McKenna SJ. Morrison K. A Comparison of Skin History and Trajectory based Representation Schemes for The Recognition of User Specified Gestures[J]. Pattern Recognition,2004,37(5):999-1009
[33]Kolsch M, Turk M. Vision Based Interfaces for Mobility[C]. Proceedings of Mobiquitous, 2004:86-94
[34]NickelK, Stiefelhagen R. Visual Recognition of Pointing Gestures for Human-Robot Interactio[J].Image and Vision Computing, 2007,25(12):1875-1884
[35]Munib Q, Habeeb M. American Sign Language(ASL) Recognition based on Hough Transform and Neural Networks[J]. Expert Systems with Applications, 2007,32(1):24-37
[36]Lichtenauer JF, Hendriks EA. Sign Language Recognition by Combining Statistical DTW and Independent Classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30(11):2040-2046
[37]任海兵,祝远新,徐光祜,等.连续动态手势的时空表观建模及识别[J].计算机学报,2000,23(8)：824-828
[38]李勇,高文,姚鸿勋.基于颜色手套的中国手指语字母的动静态识别[J].计算机工程与应用,2002,38(17)：55-58
[39]张良国,高文,陈熙霖,等.面向中等词汇量的中国手语视觉识别系统[J].计算机研究与发展,2006,43(3)：476-482
[40]王西颖,戴国忠,张习文,等.基于HMM.FNN模型的复杂动态手势识别[J].软件学报,2008,19(9)：2302-2312
[41]周宇,陈熙霖,赵德斌,等.基于数据生成的手语识别自适应方法[J].高技术通讯,2009,19(12)：1258-1264
[42]王骐,陈熙霖,王春立,等.一种可处理数据缺失的视角无关手语识别方法[J].计算机学报,2009,32(5)：953-963
[43]张秋余,胡建强,张墨逸.基于区域生长的Mean-shift动态变形手势跟踪算法[J].模式识别与人工智能,2010,23(4)：580-586
[44]Adamo Villani, N. Benes, Brisbin, et al. A Natural Interface for Sign Language Mathematics[C].2nd International Symposium on Visual Computing, Nevada, 2006, 23:1-240
[45]陈益强,高文,刘军发,等.手语合成中的多模式行为协同韵律模型[J].计算机学报,2006,29(5)：822-830
[46]颜庆聪,陈益强,刘军发.面向广电节目的虚拟人手语合成显示平台研究[J].计算机研究与发展,2009,46(11)：1893-1902
[47]宋丹.基于移动终端的中国手语合成系统[D].哈尔滨：哈尔滨工业大学,2011
[48]王振.面向中国手语合成的真实感人脸动画研究[D].北京：北京工业大学,2010
[49]F. Ciaramello, A. Cavender, S. Hemami, et al. Predicting Intelligibility of Compressed American Sign Language Video With Objective Quality Metrics [C].2006 International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, AZ, January 2006
[50]Wang Ru, Wang Lichun, Kong Dehui, et al. Information Expression Oriented toward the Hearing-Impaired Based on Sign Language Video Synthesis[J].China Communications, 2011,1:139-144
[51]倪训博,赵德斌,姜峰,等.Viterbi和DTW算法的关系分析在非特定人手语识别中的应用[J].计算机研究与发展,2010,47(2)：305-317
[52]Gaolin Fang, Wen Gao, Debin Zhao. Large Vocabulary Sign Language Recognition Based on Fuzzy Decision Trees[J]. IEEE Transactions on System Man and Cybernetics, 2004,34(3):305-314
[53]N. Cherniavsky, J. Chon, J. O. Wobbrock, et al. Activity Analysis Enabling Real-Time Video Communication on Mobile Phones for Deaf Users [C]. Symposium on User Interface Software and Technology, Victoria, British Columbia, 4-7 Oct. 2009:17-21
[54]N. Cherniavsky, A. C. Cavender, R. E. Ladner, et al. Variable Frame Rate for Low Power Mobile Sign Language Communication [C]. Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility, Tempe, Arizona, USA, 15-17 Oct. 2007:163-170
[55]N. Cherniavsky. Activity Analysis of Sign Language Video for Mobile Telecommunication [D]. Seattle:University of Washington, 2008
[56]J. Tran, T. Johnson, J. Kim, et al.A Web-Based User Survey for Evaluating Power Saving Strategies for Deaf Users of MobileASL [C]. Proceedings of ASSETS 2010:The 12th International ACM SIGACCESS Conference on Computers and Accessibility, Orlando, FL, October 25-27,2010
[57]J. J. Tran. Power Saving Strategies for Two-Way, Real-Time Video-Enabled Cellular Phones[D]. Seattle:University of Washington, 2010
[58]Rahul Vanam. Rate-Distortion-Complexity Optimization of Video Encoders with Applications to Sign Language Video Compression [D]. Seattle:University of Washington, 2010
[59]L. Muir, I. Richardson. Perception of Sign Language and its Application to Visual Communications for Deaf People [J]. Journal of Deaf Studies and Deaf Education, 2005, 10(4):390-401
[60]D. Agrafiotis, C. N. Canagarajah, D. R. Bull, et al. Optimized Sign Language Video Coding Based on Eye-Tracking Analysis [J]. Visual Communications and Image Processing, 2003:1244-1252
[61]D. Agrafiotis, N. Canagarajah, D. R. Bull, et al. A Perceptually Optimised Video Coding System for Sign Language Communication at Lowbit Rates [J]. Signal Processing:Image Communication,21(7):531-549,2006
[62]D. M. Saxe, R. A. Foulds. Robust Region of Interest Coding for Improved Sign Language Telecommunication [J]. IEEE Transactions on Information Technology in Biomedicine,2002,6(4):310-316
[63]陈晓雷,张爱华,林冬梅,等.多优先级感兴趣区H.264计算资源分配方法[J].计算机工程,2013,39(4)：283-287
[64]杨继珩.面向手语新闻播报系统的压缩技术研究[D].北京：北京工业大学,2007
[65]Frank M. Ciaramello, Sheila S. Hemami. A Computational Intelligibility Model for Assessment and Compression of American Sign Language Video[J]. IEEE Transactions on Image Processing, 2011,20(11):3014-3028
[66]F. Ciaramello, R. Vanam, J. Chon, et al. Rate-Distortion-Complexity Optimization of An H.264/AVC Encoder for Real-Time Video Conferencing on a Mobile Device[C].5th International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, AZ, January 2010
[67]R. Vanam, F. Ciaramello, E. Riskin, et al. Joint Rate-Intelligibility-Complexity Optimization of an H.264 Video Encoder for American Sign Language[C].Western New York Image Processing Workshop, Rochester, NY, September 2009
[68]R. Vanam. Rate-Distortion-Complexity Optimization of Video Encoders with Applications to Sign Language Video Compression[D]. Seattle:University of Washington, 2010
[69]T. Wiegand, G. J. Sullivan, G. Bjntegaard, et al. Overview of The H.264/AVC Video Coding Standard [J]. IEEE Transactions on Circuits and System for Video Technology, 2003,13(7):560-576
[70]K. Hari, K. Philip, J. Rashad, et al. Low Complexity H. 264 Intra MB Coding[C].Proceedings of the International Conference of Consumer Electronics, Las Vegas, NV, United States,9-13 Jan.2008:1-2
[71]M. G Sarwer, L. M. Po. J. Wu. Complexity Reduced Mode Selection of H.264/AVC Intra Coding[C]Proceedings of the International Conference on Audio, Language and Image Processing, Shanghai, China,7-9 July.2008:1492-1496
[72]M. Parlak, Y. Adibelli, I. Hamzaoglu. A Novel Computational Complexity and Power Reduction Technique for H.264 Intra Prediction[J]. IEEE Transactions on Consumer Electronics.2008.54(4):2006-2014
[73]X. J. Gao, K. M. Lam, L. Zhuo, et al. Complexity Scalable Control Algorithm for Intra Coding in H.264 under Energy Constraints[J].High Technology Letters.2010,16 (3): 74-279
[74]崔玉斌,蔡安妮.一种新颖的H.264帧内预测快速算法[J].北京邮电大学学报,2008,31(2)：118-204
[75]元辉,常义林,卢朝阳,等.一种降低预测模式开销的帧内预测方法[J].西安电子科技大学学报(自然科学版),2010,37(6)：981-989
[76]张志涛,粱光明,陈明生,等.基于纹理特征的H.264帧内预测快速算法[J].中国图象图形学报,2011,16(8)：1369-1373
[77]周巍,周欣,段哲民.基于H.264/AVC的帧内4×4预测模式快速选择算法[J].西北工业大学学报,2012,30：440-445
[77]詹舒波,宋建斌,马丽,等.基于频域和空域分析的帧内预测模式快速选择算法[J].通信学报,2012,33(7)：143-152
[79]宋云,沈燕飞,龙际珍,等.基于方向梯度的H.264帧内预测模式选择算法[J].计算机学报,2013,36(8)：1757-1764
[80]S. Saponara, M. Casula, F. Rovati, et al. Dynamic Control of Motion Estimation Search Parameters for Low Complex H.264 Video Coding[J].IEEE Transactions on Consumer Electronics,2006,52(1):232-239
[81]F. A. Hasan, A. Yucel. Rate-Distortion and Complexity Optimized Motion Estimation for H. 264 Video Coding[J]. IEEE Transactions on Circuits and Systems for Video Technology,2008,18(2):159-171
[82]V. Rahul, A. R. Eve, S. H. Sheila. Distortion-Complexity Optimization of the H.264/MPEG-4 AVC Encoder Using the GBFOS Algorithm[C]. Proceedings of the International Data Compression Conference, Snowbird, UT,United States, Mar.2007: 303-312
[83]周巍,史浩山,周欣.H.264帧间预测快速算法[J].计算机辅助设计与图形学学报,2008,20(6)：770-775
[84]顾梅花,余宁梅,寇立康,等.H.264快速模式选择算法中的提前终止策略[J].中国图象图形学报,2011,16(3)：305-310
[85]刘鹏宇,何絮,贾克斌.对特定模式进行预判的H.264帧间快速编码算法[J].兵工学报,2011,32(4)：439-45
[86]吴笛,卿粼波,何小海.基于MVMW的H.264/AVC自适应快速帧间模式决策算法[J].系统工程与电子技术,2013,35(6)：1330-1336
[87]何书前,倪江群,石春.一种分层判决结构的H.264/AVC快速帧间模式选择方法[J].电子学报,2013,41(1)：2199-2208
[88]J. Stottrup-Andersen, S. Forchhammer, and S. M. Aghito, Rate-Distortion-Complexity Optimization of Fast Motion Estimation in H.264/MPEG-4 AVC[C].Proceedings of IEEE International Conference Image Process, October 2004:111-114
[89]Y. Hu, Q. Li, S. Ma, et al Joint Rate-Distortion-Complexity Optimization for H.264 Motion Search[C].Proceedings of IEEE International Conference on Multimedia and Expo,2006:1949-1952
[90]Kim C, Xin J, Vetro A, et al. Complexity Scable Motion Estimation for H.264/AVC [C]. Proceedings of SPIE Conference Visual Communications and Image Processing, 2006, 6077:109-120
[91]Saponara S, Casula M, Rovati F. Dynamic Control of Motion Estimation Search Parameters for Low Complex H.264 video coding [J]. IEEE Transactions on Consumer Electronics,2006,52(1):232-239
[92]T.-C. Chen, Y.-H. Chen, S.-F. Tsai, et al. Fast Algorithm and Architecture Design of Low Power Integer Motion Estimation for H.264/AVC[J].IEEE Transactions on Circuits and Systems for Video Technology,2007,17(5):568-577
[93]R. Vanam, E. A. Riskin, S. S. Hemami, et al Distortion-Complexity Optimization of The H.264/MPEG-4 AVC Encoder Using the Gbfos Algorithm[C] Proceedings of IEEE Data Compression Conference, March 2007:303-312
[94]Chen Z X, Song Y, Ikenaga T, et al A Dynamic Search Range Algorithm for Variable Block Size Motion Estimation in H.264/AVC [C]. International Conference on Information, Communications&Signal Processing, 2007:1-4
[95]Zhang D M, Lin S X, Zhang Y D, et al. Complexity Controllable DCT for Real-Time H.264 encoder [J]. Journal of Visual Communication and Image Representation, 2007, 18(1):59-67
[96]Su L, Lu Y, Wu F. Real-Time Video Coding under Power Constraint based on H.264 Codec [C]. Proceedings of SPIE Conference Visual Communications and Image Processing,2007,6508:1-12
[97]H. Ates and Y. Altunbasak. Rate-Distortion and Complexity Optimized Motion Estimation for H.264 Video Coding IEEE Transactions on Circuits and Systems for VideoTechnology[J],2008,18(2):159-171
[98]杨春玲,王华兴.基于结构相似度的H.264快速运动估计算法[J].华南理工大学学报(自然科学版),2008,36(8)：28-35
[99]韦耿,刘文予,李鹏飞.基于运动区域划分的H.264低复杂度模式选择[J].计算机辅助设计与图形学学报,2008,20(1)：93-100
[100]张淑芳,李华.基于H.264的多参考帧快速选择算法[J].电子学报,2009,37(1)：62-66
[101]吴晓军,白世军,卢文涛.基于H.264视频编码的运动估计算法优化[J].电子学报,2009,37(11)：2541-2546
[102]张庆明,彭强.运用H.264/AVC宏块编码特征的低复杂度率失真优化算法[J].中国图象图形学报,2011,16(5)；733-739
[103]高雪娟.面向无线移动终端的H.264编码复杂度控制技术研究[D].北京：北京工业大学,2009
[104]Sachin P. Kamat. Energy Management Architecture for Multimedia Applications in Battery Powered Devices[J].IEEE Transactions on Consumer Electronics, 2009, 55(2):763-767
[105]Wen Ji, Min Chen, Xiaohu Ge, et al.ESVD:An Integrated Energy Scalable Framework for Low-Power Video Decoding Systems[J]. EURASIP Journal on Wireless Communications and Networking, 2010:5
[106]Muhammad Shafique, Lars Bauer, and Jorg Henkel. 3-Tier Dynamically Adaptive Power-Aware Motion Estimator for H.264/AVC Video Encoding[C]. Proceedings of the 13th International Symposium on Low Power Electronics and Design, New York, NY, USA 2008:147-152
[107]Z. Chen, P. Zhou, Y. He. Fast Integer Pel and Fractional Pel Motion Estimation for JVT[Z]. JVT-F017, December, 2002
[108]A. M. Tourapis. Enhanced Predictive Zonal Search for Single and Multiple Frame Motion Estimation [C]. Proceedings of VCIP, 2002:1069-1079
[109]H.264 reference software JM16.2 http://iphome.hdi.de/suehring/tml/download [EB/OL]
[110]SiouShen Lin, PoChih Tseng, ChiaPing Lin, et al. Multi-Mode Content Aware Motion Estimation Algorithm for Power-Aware Video Coding Systems[C]. IEEE Workshop on Signal Processing Systems, 2004:239-244
[111]M. Corbetta and G. L. Shulman. Control of Goal-Oriented and Stimulus-Driven Attention in The Brain[J].Nature Reviews Neuroscience.,2002,3(3):201-215
[112]Liu Y,Li Z QSoh Y Conversational Video Communication of H.264/AVC with Region-of-Interest Concern[C].IEEE International Conference on Image Processing, 2006:3129-3132
[113]Tang C W,Chen C H,Yu Y H.Visual Sensitivity Guided Bit Allocation for Video Coding[J].IEEE Transactions on Multimedia, 2006,8(1):11-18
[114]Tang C W. Spatio Temporal Visual Consideration for Video Coding [J], IEEE Transactions on Multimedia,2007,9(2):231-238
[115]M. Jiang, N. Ling. On Enhancing H.264/AVC Video Rate Control by PSNR-Based Frame Complexity Estimation[J]. IEEE Transactions on Consumer Electronics, 2005, 51:281-287
[116]Y. Sun, A. Ishfaq, D. D. Li.Region based Rate Control and Bit Allocation for Wireless Video Transmission[J]. IEEE Transactions on Multimedia,2006,8:1-10
[117]陶霖密,彭振云,徐广裕.人体的肤色特征[J].软件学报,2001,12(7)：1032-1041
[118]Z. Sun, X. Chen, and Z. He. Adaptive Critic Design for Energy Minimization of Portable Video Communication Devices[J].IEEE Transactions on Circuits and Systems for Video Technology,2010,20(1):27-37
[119]Sun J, Wang X, Ci S. Battery-Aware Multimedia Coding Optimization by Dynamic Frequency Scaling[C]. Proceedings of 20th International Conference on Computer Communications and Networks. IEEE,2011:1-6
[120]韦耿,王亮,朱斌.无线移动环境视频编码动态功耗模型研究[J].传感技术学报,2009,22(3)：351-354
[121]Li-Wei Kang, Chun-Shien Lu, Chih-Yang Lin. Low-Complexity Video Coding via Power-Rate-Distortion Optimization[J].Journal of Visual Communication and Image Representation,2012,23:569-585
[122]X. Guo, Y. Lu, F. Wu, D. Zhao, et al. Wyner-Ziv-based Multi-View Video Coding[J].IEEE Transaction on Circuits and Systems for Video Technology, 2008, 18(6):713-724
[123]Habili N, Lim C C, Moini A. Segmentation of The Face and Hands in Sign Language Video Sequences using Color and Motion Cues [J].IEEE Transactions on Circuits and Systems for Video Technology,2004,14(8):086-1097
[124]曹听燕,赵继印,李敏.基于肤色和运动检测技术的单目视觉手势分割[J].湖南大学学报：自然科学版,2011,38(1)：78-83
[125]张爱华,雷小亚,陈晓雷,等.基于细胞神经网络的快速手语视频分割方法[J].计算机应用,2013,33(02)：503-506
[126]Holden E, Lee G, Owens R. Australian Sign Language Recognition [J]. Machine Vision and Applications,2005,16 (5):312-320
[127]Gonzalez M, Collet C, Dubot R. Head Tracking and Hand Segmentation during Hand over Face Occlusion in Sign Language [J] Lecture Notes in Computer Science, 2012, 6553:234-243
[128]Smith P, Lobo N, Shah M. Resolving Hand over Face Occlusion [J]. Image and Vision Computing 2007(25):1432-1448
[129]A Hussain, Abbasi A R, Afzulpurkar N. Detecting and Interpreting Self-Manipulating Hand Movements for Student's Affect Prediction [J].Human-centric Computing and Information Sciences 2012,2(1):14
[130]Hurley D J, Nixon M S, Carter J N. Force Field Feature Extraction for Ear Biometrics[J]. Computer Vision and Image Understanding,2005,98(3):491-512
[131]朱海华,李雅娟,宋志坚.基于图像力场转换的耳廓图像识别[J].自动化学报,2006,32(4)：512-518
[132]莫兴俊.万有引力在人耳图像识别中的应用研究[D].重庆：重庆大学,2007
[133]董冀媛,穆志纯,王瑜.基于力场收敛特征的多姿态人耳识别[J].计算机应用研究,2009,26(6)：2370-2375
[134]Nixon M S, Liu X U, Direkoglu C et al. On Using Physical Analogies for Feature and Shape Extraction in Computer Vision [J].The Computer Journal,2011,54(1):11-25,
[135]葛玉红.大家学手语[EB/OL].(2008-10-01)[2013-06-28]
[2]Zhihai He, Sanjit K Mitra. From Rate-Distortion Analysis to Resource-Distortion Analysis [J]. IEEE Circuits and Systems Magazine, 2005,5(3):6-18
[3]Wenye Cheng, Xi Chen, Zhihai He. Doubling of the Operational Lifetime of Portable Video Communication Devices Using Power-Rate-Distortion Analysis and Control[C]. IEEE International Conference on Image Processing, October, 2006, 2473-2476
[4]Zhihai He, Wenye Cheng, Xi Chen. Energy Minimization of Portable Video Communication Devices Based on Power-Rate-Distortion Optimization [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2008,18(5):596-608
[5]Yiran Li, Guiqiang Dong, and Tong Zhang. Joint Source-Channel Coding and Channelization for Embedded Video Processing With Flash Memory Storage[J]. IEEE Transactions on Image Processing, 2012,60(8):4403-4415
[6]Younghoon Lee, Jungsoo Kim, and Chong-Min Kyung. Energy-Aware Video Encoding for Image Quality Improvement in Battery-Operated Surveillance Camera[J]. IEEE Transactions on Very Large Scale Integration Systems, 2012,20(2):310-319
[7]Junni Zou, Hongkai Xiong, Chenglin Li, et al.Lifetime and Distortion Optimization With Joint Source/Channel Rate Adaptation and Network Coding-Based Error Control in Wireless Video Sensor Networks[J]. IEEE Transactions on Vehicular Technology, 2011, 60(3):1182-1195
[8]Malisa Marijan, Ilker Demirkol, Danijel Maricic, et al. Adaptive Sensing and Optimal Power Allocation for Wireless Video Sensors With Sigma-Delta Imager[J]. IEEE Transactions on Image Processing, 2010,19(10):2540-2555
[9]Xiang Li, Mathias Wien, Jens-Rainer Ohm. Rate-Complexity-Distortion Optimization for Hybrid Video Coding[J].IEEE Transactions on Circuits and Systems for Video Technology, 2011,21(7):957-971
[10]陆寄远,张培钊,段晓华,等.一种复杂度约束下基于宏块优先顺序的运动估计优化算法[J].计算机研究与发展,2011,48(3)：494-500
[11]Xuejuan Gao,Kin Man Lam, Li Zhuo, et al. Complexity scalable control for H.264 motion estimation and mode decision under energy constraints[J].Signal Processing, 2010,90(8):2468-2479
[12]Chungjr Lian, Pochih Tseng, Lianggee Chen. Low-Power and Power-A ware Video Codec Design: An Overview [J].China Communications, 2006 October: 45-51
[13]Chungjr Lian, Shaoyi Chien, Chiaping Lin, et al. Power-Aware Multimedia:Concepts and Perspectives [J]. IEEE Circuits and Systems Magazine, 2007,7(2):26-34
[14]Yuhan Chen, Tungchien Chen, Chuanyung Tsai, et al. Algorithm and Architecture Design of Power-Oriented H.264/AVC Baseline Profile Encoder for Portable Devices [J].IEEE Transactions on Circuits and Systems for Video Technology, 2009, 19(8): 1118-1128
[15]Wen Ji, Jiangchuan Liu, Min Chen, et al. Power-Efficient Video Encoding on Resource-Limited Systems:A Game-Theoretic Approach[J].Future Generation Computer Systems,2012,28:427-436
[16]Yang Liu, Zheng Guo Li, Yeng Chai Soh. Region-of-Interest based H.264 Encoding Parameter Allocation for Low Power Video Communication[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2008,18(1):134-139
[17]Avin Kumar Kannur, Baoxin Li. Power-A ware Content-Adaptive H.264 Video Encoding[C].IEEE International Conference on Acoustics, Speech and Signal Processing, 2009,925-928
[18]Minghui Wang, Tianruo Zhang, Chen Liu, et al. Region-of-Interest Based H.264 Encoding Parameter Allocation for Low Power Video Communication[C]. The 5th International Colloquium on Signal Processing and Its Applications, March 2009, 233-237
[19]Yayu Zheng, Fan Zhou, Xiang Tian, et al. Lightweight Content-Adaptive Coding in Joint Analyzing-Encoding Framework[J]. IEEE Transactions on Consumer Electronics, 2008, 54(2):614-622
[20]A. Cavender, R. Ladner, E. Riskin.MobileASL:Intelligibility of Sign Language Video as Constrained by Mobile Phone Technology[C]. Proceedings of ASSETS:The Eighth International ACM SIGACCESS Conference on Computers and Accessibility, October 2006:71-78
[21]R.Vanam, F.Ciaramello, E. Riskin, et al. Joint Rate-Intelligibility-Complexity Optimization of an H.264 Video Encoder for American Sign Language[C]. Western New York Image Processing Workshop (WNYIP), Rochester, NY, September 2009
[22]J. Chon, N. Cherniavsky, E. Riskin, et al.. Enabling Access through Real-Time Sign Language Communication over Cell Phones[C].43rd Annual Asilomar Conference on Signals, Systems, and Computers, November 2009:588-592
[23]K. Emmorey, R. Thompson, and R. Colvin. Eye Gaze during Comprehension of American Sign Language by Native and Beginning signers [J]. Journal of Deaf Students and Deaf Education,2009,14(2):237-243
[24]Davide Bottari, Matteo Valsecchi and Francesco Pavani. Prominent Reflexive Eye-Movement Orienting Associated with Deafness[J].Cognitive Neuroscience, 2012 3(1):8-13
[25]黎洪松.数字视频处理[M].北京邮电大学出版社,2006
[26]李斌.面向高性能视频编码标准的率失真优化技术研究[D].合肥：中国科学技术大学,2013
[27]韦耿.视频编码功率率失真模型及低复杂度算法研究[D].武汉：华中科技大学,2007
[28]C. L. James ,K. M. Reischel. Text Input for Mobile Devices:Comparing Model Prediction to Actual Performance[C]. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2001:365-371
[29]Starner T,Weaver J,Pentland A. Real-time American Sign Language Recognition Using Desk and Wearable Computer Based Video[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998,20(12):1371-1375
[30]Lee HK. Kim JH. An HMM Based Threshold Model Approach for Gesture Recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(10):961-973
[31]Lankton R, Fitzgibbon AW. Real-Time Gesture Recognition Using Deterministic Boosting[C]. Proceeding of British Machine Vision Conference, 2002:817-826
[32]McKenna SJ. Morrison K. A Comparison of Skin History and Trajectory based Representation Schemes for The Recognition of User Specified Gestures[J]. Pattern Recognition,2004,37(5):999-1009
[33]Kolsch M, Turk M. Vision Based Interfaces for Mobility[C]. Proceedings of Mobiquitous, 2004:86-94
[34]NickelK, Stiefelhagen R. Visual Recognition of Pointing Gestures for Human-Robot Interactio[J].Image and Vision Computing, 2007,25(12):1875-1884
[35]Munib Q, Habeeb M. American Sign Language(ASL) Recognition based on Hough Transform and Neural Networks[J]. Expert Systems with Applications, 2007,32(1):24-37
[36]Lichtenauer JF, Hendriks EA. Sign Language Recognition by Combining Statistical DTW and Independent Classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30(11):2040-2046
[37]任海兵,祝远新,徐光祜,等.连续动态手势的时空表观建模及识别[J].计算机学报,2000,23(8)：824-828
[38]李勇,高文,姚鸿勋.基于颜色手套的中国手指语字母的动静态识别[J].计算机工程与应用,2002,38(17)：55-58
[39]张良国,高文,陈熙霖,等.面向中等词汇量的中国手语视觉识别系统[J].计算机研究与发展,2006,43(3)：476-482
[40]王西颖,戴国忠,张习文,等.基于HMM.FNN模型的复杂动态手势识别[J].软件学报,2008,19(9)：2302-2312
[41]周宇,陈熙霖,赵德斌,等.基于数据生成的手语识别自适应方法[J].高技术通讯,2009,19(12)：1258-1264
[42]王骐,陈熙霖,王春立,等.一种可处理数据缺失的视角无关手语识别方法[J].计算机学报,2009,32(5)：953-963
[43]张秋余,胡建强,张墨逸.基于区域生长的Mean-shift动态变形手势跟踪算法[J].模式识别与人工智能,2010,23(4)：580-586
[44]Adamo Villani, N. Benes, Brisbin, et al. A Natural Interface for Sign Language Mathematics[C].2nd International Symposium on Visual Computing, Nevada, 2006, 23:1-240
[45]陈益强,高文,刘军发,等.手语合成中的多模式行为协同韵律模型[J].计算机学报,2006,29(5)：822-830
[46]颜庆聪,陈益强,刘军发.面向广电节目的虚拟人手语合成显示平台研究[J].计算机研究与发展,2009,46(11)：1893-1902
[47]宋丹.基于移动终端的中国手语合成系统[D].哈尔滨：哈尔滨工业大学,2011
[48]王振.面向中国手语合成的真实感人脸动画研究[D].北京：北京工业大学,2010
[49]F. Ciaramello, A. Cavender, S. Hemami, et al. Predicting Intelligibility of Compressed American Sign Language Video With Objective Quality Metrics [C].2006 International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, AZ, January 2006
[50]Wang Ru, Wang Lichun, Kong Dehui, et al. Information Expression Oriented toward the Hearing-Impaired Based on Sign Language Video Synthesis[J].China Communications, 2011,1:139-144
[51]倪训博,赵德斌,姜峰,等.Viterbi和DTW算法的关系分析在非特定人手语识别中的应用[J].计算机研究与发展,2010,47(2)：305-317
[52]Gaolin Fang, Wen Gao, Debin Zhao. Large Vocabulary Sign Language Recognition Based on Fuzzy Decision Trees[J]. IEEE Transactions on System Man and Cybernetics, 2004,34(3):305-314
[53]N. Cherniavsky, J. Chon, J. O. Wobbrock, et al. Activity Analysis Enabling Real-Time Video Communication on Mobile Phones for Deaf Users [C]. Symposium on User Interface Software and Technology, Victoria, British Columbia, 4-7 Oct. 2009:17-21
[54]N. Cherniavsky, A. C. Cavender, R. E. Ladner, et al. Variable Frame Rate for Low Power Mobile Sign Language Communication [C]. Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility, Tempe, Arizona, USA, 15-17 Oct. 2007:163-170
[55]N. Cherniavsky. Activity Analysis of Sign Language Video for Mobile Telecommunication [D]. Seattle:University of Washington, 2008
[56]J. Tran, T. Johnson, J. Kim, et al.A Web-Based User Survey for Evaluating Power Saving Strategies for Deaf Users of MobileASL [C]. Proceedings of ASSETS 2010:The 12th International ACM SIGACCESS Conference on Computers and Accessibility, Orlando, FL, October 25-27,2010
[57]J. J. Tran. Power Saving Strategies for Two-Way, Real-Time Video-Enabled Cellular Phones[D]. Seattle:University of Washington, 2010
[58]Rahul Vanam. Rate-Distortion-Complexity Optimization of Video Encoders with Applications to Sign Language Video Compression [D]. Seattle:University of Washington, 2010
[59]L. Muir, I. Richardson. Perception of Sign Language and its Application to Visual Communications for Deaf People [J]. Journal of Deaf Studies and Deaf Education, 2005, 10(4):390-401
[60]D. Agrafiotis, C. N. Canagarajah, D. R. Bull, et al. Optimized Sign Language Video Coding Based on Eye-Tracking Analysis [J]. Visual Communications and Image Processing, 2003:1244-1252
[61]D. Agrafiotis, N. Canagarajah, D. R. Bull, et al. A Perceptually Optimised Video Coding System for Sign Language Communication at Lowbit Rates [J]. Signal Processing:Image Communication,21(7):531-549,2006
[62]D. M. Saxe, R. A. Foulds. Robust Region of Interest Coding for Improved Sign Language Telecommunication [J]. IEEE Transactions on Information Technology in Biomedicine,2002,6(4):310-316
[63]陈晓雷,张爱华,林冬梅,等.多优先级感兴趣区H.264计算资源分配方法[J].计算机工程,2013,39(4)：283-287
[64]杨继珩.面向手语新闻播报系统的压缩技术研究[D].北京：北京工业大学,2007
[65]Frank M. Ciaramello, Sheila S. Hemami. A Computational Intelligibility Model for Assessment and Compression of American Sign Language Video[J]. IEEE Transactions on Image Processing, 2011,20(11):3014-3028
[66]F. Ciaramello, R. Vanam, J. Chon, et al. Rate-Distortion-Complexity Optimization of An H.264/AVC Encoder for Real-Time Video Conferencing on a Mobile Device[C].5th International Workshop on Video Processing and Quality Metrics for Consumer Electronics, Scottsdale, AZ, January 2010
[67]R. Vanam, F. Ciaramello, E. Riskin, et al. Joint Rate-Intelligibility-Complexity Optimization of an H.264 Video Encoder for American Sign Language[C].Western New York Image Processing Workshop, Rochester, NY, September 2009
[68]R. Vanam. Rate-Distortion-Complexity Optimization of Video Encoders with Applications to Sign Language Video Compression[D]. Seattle:University of Washington, 2010
[69]T. Wiegand, G. J. Sullivan, G. Bjntegaard, et al. Overview of The H.264/AVC Video Coding Standard [J]. IEEE Transactions on Circuits and System for Video Technology, 2003,13(7):560-576
[70]K. Hari, K. Philip, J. Rashad, et al. Low Complexity H. 264 Intra MB Coding[C].Proceedings of the International Conference of Consumer Electronics, Las Vegas, NV, United States,9-13 Jan.2008:1-2
[71]M. G Sarwer, L. M. Po. J. Wu. Complexity Reduced Mode Selection of H.264/AVC Intra Coding[C]Proceedings of the International Conference on Audio, Language and Image Processing, Shanghai, China,7-9 July.2008:1492-1496
[72]M. Parlak, Y. Adibelli, I. Hamzaoglu. A Novel Computational Complexity and Power Reduction Technique for H.264 Intra Prediction[J]. IEEE Transactions on Consumer Electronics.2008.54(4):2006-2014
[73]X. J. Gao, K. M. Lam, L. Zhuo, et al. Complexity Scalable Control Algorithm for Intra Coding in H.264 under Energy Constraints[J].High Technology Letters.2010,16 (3): 74-279
[74]崔玉斌,蔡安妮.一种新颖的H.264帧内预测快速算法[J].北京邮电大学学报,2008,31(2)：118-204
[75]元辉,常义林,卢朝阳,等.一种降低预测模式开销的帧内预测方法[J].西安电子科技大学学报(自然科学版),2010,37(6)：981-989
[76]张志涛,粱光明,陈明生,等.基于纹理特征的H.264帧内预测快速算法[J].中国图象图形学报,2011,16(8)：1369-1373
[77]周巍,周欣,段哲民.基于H.264/AVC的帧内4×4预测模式快速选择算法[J].西北工业大学学报,2012,30：440-445
[77]詹舒波,宋建斌,马丽,等.基于频域和空域分析的帧内预测模式快速选择算法[J].通信学报,2012,33(7)：143-152
[79]宋云,沈燕飞,龙际珍,等.基于方向梯度的H.264帧内预测模式选择算法[J].计算机学报,2013,36(8)：1757-1764
[80]S. Saponara, M. Casula, F. Rovati, et al. Dynamic Control of Motion Estimation Search Parameters for Low Complex H.264 Video Coding[J].IEEE Transactions on Consumer Electronics,2006,52(1):232-239
[81]F. A. Hasan, A. Yucel. Rate-Distortion and Complexity Optimized Motion Estimation for H. 264 Video Coding[J]. IEEE Transactions on Circuits and Systems for Video Technology,2008,18(2):159-171
[82]V. Rahul, A. R. Eve, S. H. Sheila. Distortion-Complexity Optimization of the H.264/MPEG-4 AVC Encoder Using the GBFOS Algorithm[C]. Proceedings of the International Data Compression Conference, Snowbird, UT,United States, Mar.2007: 303-312
[83]周巍,史浩山,周欣.H.264帧间预测快速算法[J].计算机辅助设计与图形学学报,2008,20(6)：770-775
[84]顾梅花,余宁梅,寇立康,等.H.264快速模式选择算法中的提前终止策略[J].中国图象图形学报,2011,16(3)：305-310
[85]刘鹏宇,何絮,贾克斌.对特定模式进行预判的H.264帧间快速编码算法[J].兵工学报,2011,32(4)：439-45
[86]吴笛,卿粼波,何小海.基于MVMW的H.264/AVC自适应快速帧间模式决策算法[J].系统工程与电子技术,2013,35(6)：1330-1336
[87]何书前,倪江群,石春.一种分层判决结构的H.264/AVC快速帧间模式选择方法[J].电子学报,2013,41(1)：2199-2208
[88]J. Stottrup-Andersen, S. Forchhammer, and S. M. Aghito, Rate-Distortion-Complexity Optimization of Fast Motion Estimation in H.264/MPEG-4 AVC[C].Proceedings of IEEE International Conference Image Process, October 2004:111-114
[89]Y. Hu, Q. Li, S. Ma, et al Joint Rate-Distortion-Complexity Optimization for H.264 Motion Search[C].Proceedings of IEEE International Conference on Multimedia and Expo,2006:1949-1952
[90]Kim C, Xin J, Vetro A, et al. Complexity Scable Motion Estimation for H.264/AVC [C]. Proceedings of SPIE Conference Visual Communications and Image Processing, 2006, 6077:109-120
[91]Saponara S, Casula M, Rovati F. Dynamic Control of Motion Estimation Search Parameters for Low Complex H.264 video coding [J]. IEEE Transactions on Consumer Electronics,2006,52(1):232-239
[92]T.-C. Chen, Y.-H. Chen, S.-F. Tsai, et al. Fast Algorithm and Architecture Design of Low Power Integer Motion Estimation for H.264/AVC[J].IEEE Transactions on Circuits and Systems for Video Technology,2007,17(5):568-577
[93]R. Vanam, E. A. Riskin, S. S. Hemami, et al Distortion-Complexity Optimization of The H.264/MPEG-4 AVC Encoder Using the Gbfos Algorithm[C] Proceedings of IEEE Data Compression Conference, March 2007:303-312
[94]Chen Z X, Song Y, Ikenaga T, et al A Dynamic Search Range Algorithm for Variable Block Size Motion Estimation in H.264/AVC [C]. International Conference on Information, Communications&Signal Processing, 2007:1-4
[95]Zhang D M, Lin S X, Zhang Y D, et al. Complexity Controllable DCT for Real-Time H.264 encoder [J]. Journal of Visual Communication and Image Representation, 2007, 18(1):59-67
[96]Su L, Lu Y, Wu F. Real-Time Video Coding under Power Constraint based on H.264 Codec [C]. Proceedings of SPIE Conference Visual Communications and Image Processing,2007,6508:1-12
[97]H. Ates and Y. Altunbasak. Rate-Distortion and Complexity Optimized Motion Estimation for H.264 Video Coding IEEE Transactions on Circuits and Systems for VideoTechnology[J],2008,18(2):159-171
[98]杨春玲,王华兴.基于结构相似度的H.264快速运动估计算法[J].华南理工大学学报(自然科学版),2008,36(8)：28-35
[99]韦耿,刘文予,李鹏飞.基于运动区域划分的H.264低复杂度模式选择[J].计算机辅助设计与图形学学报,2008,20(1)：93-100
[100]张淑芳,李华.基于H.264的多参考帧快速选择算法[J].电子学报,2009,37(1)：62-66
[101]吴晓军,白世军,卢文涛.基于H.264视频编码的运动估计算法优化[J].电子学报,2009,37(11)：2541-2546
[102]张庆明,彭强.运用H.264/AVC宏块编码特征的低复杂度率失真优化算法[J].中国图象图形学报,2011,16(5)；733-739
[103]高雪娟.面向无线移动终端的H.264编码复杂度控制技术研究[D].北京：北京工业大学,2009
[104]Sachin P. Kamat. Energy Management Architecture for Multimedia Applications in Battery Powered Devices[J].IEEE Transactions on Consumer Electronics, 2009, 55(2):763-767
[105]Wen Ji, Min Chen, Xiaohu Ge, et al.ESVD:An Integrated Energy Scalable Framework for Low-Power Video Decoding Systems[J]. EURASIP Journal on Wireless Communications and Networking, 2010:5
[106]Muhammad Shafique, Lars Bauer, and Jorg Henkel. 3-Tier Dynamically Adaptive Power-Aware Motion Estimator for H.264/AVC Video Encoding[C]. Proceedings of the 13th International Symposium on Low Power Electronics and Design, New York, NY, USA 2008:147-152
[107]Z. Chen, P. Zhou, Y. He. Fast Integer Pel and Fractional Pel Motion Estimation for JVT[Z]. JVT-F017, December, 2002
[108]A. M. Tourapis. Enhanced Predictive Zonal Search for Single and Multiple Frame Motion Estimation [C]. Proceedings of VCIP, 2002:1069-1079
[109]H.264 reference software JM16.2 http://iphome.hdi.de/suehring/tml/download [EB/OL]
[110]SiouShen Lin, PoChih Tseng, ChiaPing Lin, et al. Multi-Mode Content Aware Motion Estimation Algorithm for Power-Aware Video Coding Systems[C]. IEEE Workshop on Signal Processing Systems, 2004:239-244
[111]M. Corbetta and G. L. Shulman. Control of Goal-Oriented and Stimulus-Driven Attention in The Brain[J].Nature Reviews Neuroscience.,2002,3(3):201-215
[112]Liu Y,Li Z QSoh Y Conversational Video Communication of H.264/AVC with Region-of-Interest Concern[C].IEEE International Conference on Image Processing, 2006:3129-3132
[113]Tang C W,Chen C H,Yu Y H.Visual Sensitivity Guided Bit Allocation for Video Coding[J].IEEE Transactions on Multimedia, 2006,8(1):11-18
[114]Tang C W. Spatio Temporal Visual Consideration for Video Coding [J], IEEE Transactions on Multimedia,2007,9(2):231-238
[115]M. Jiang, N. Ling. On Enhancing H.264/AVC Video Rate Control by PSNR-Based Frame Complexity Estimation[J]. IEEE Transactions on Consumer Electronics, 2005, 51:281-287
[116]Y. Sun, A. Ishfaq, D. D. Li.Region based Rate Control and Bit Allocation for Wireless Video Transmission[J]. IEEE Transactions on Multimedia,2006,8:1-10
[117]陶霖密,彭振云,徐广裕.人体的肤色特征[J].软件学报,2001,12(7)：1032-1041
[118]Z. Sun, X. Chen, and Z. He. Adaptive Critic Design for Energy Minimization of Portable Video Communication Devices[J].IEEE Transactions on Circuits and Systems for Video Technology,2010,20(1):27-37
[119]Sun J, Wang X, Ci S. Battery-Aware Multimedia Coding Optimization by Dynamic Frequency Scaling[C]. Proceedings of 20th International Conference on Computer Communications and Networks. IEEE,2011:1-6
[120]韦耿,王亮,朱斌.无线移动环境视频编码动态功耗模型研究[J].传感技术学报,2009,22(3)：351-354
[121]Li-Wei Kang, Chun-Shien Lu, Chih-Yang Lin. Low-Complexity Video Coding via Power-Rate-Distortion Optimization[J].Journal of Visual Communication and Image Representation,2012,23:569-585
[122]X. Guo, Y. Lu, F. Wu, D. Zhao, et al. Wyner-Ziv-based Multi-View Video Coding[J].IEEE Transaction on Circuits and Systems for Video Technology, 2008, 18(6):713-724
[123]Habili N, Lim C C, Moini A. Segmentation of The Face and Hands in Sign Language Video Sequences using Color and Motion Cues [J].IEEE Transactions on Circuits and Systems for Video Technology,2004,14(8):086-1097
[124]曹听燕,赵继印,李敏.基于肤色和运动检测技术的单目视觉手势分割[J].湖南大学学报：自然科学版,2011,38(1)：78-83
[125]张爱华,雷小亚,陈晓雷,等.基于细胞神经网络的快速手语视频分割方法[J].计算机应用,2013,33(02)：503-506
[126]Holden E, Lee G, Owens R. Australian Sign Language Recognition [J]. Machine Vision and Applications,2005,16 (5):312-320
[127]Gonzalez M, Collet C, Dubot R. Head Tracking and Hand Segmentation during Hand over Face Occlusion in Sign Language [J] Lecture Notes in Computer Science, 2012, 6553:234-243
[128]Smith P, Lobo N, Shah M. Resolving Hand over Face Occlusion [J]. Image and Vision Computing 2007(25):1432-1448
[129]A Hussain, Abbasi A R, Afzulpurkar N. Detecting and Interpreting Self-Manipulating Hand Movements for Student's Affect Prediction [J].Human-centric Computing and Information Sciences 2012,2(1):14
[130]Hurley D J, Nixon M S, Carter J N. Force Field Feature Extraction for Ear Biometrics[J]. Computer Vision and Image Understanding,2005,98(3):491-512
[131]朱海华,李雅娟,宋志坚.基于图像力场转换的耳廓图像识别[J].自动化学报,2006,32(4)：512-518
[132]莫兴俊.万有引力在人耳图像识别中的应用研究[D].重庆：重庆大学,2007
[133]董冀媛,穆志纯,王瑜.基于力场收敛特征的多姿态人耳识别[J].计算机应用研究,2009,26(6)：2370-2375
[134]Nixon M S, Liu X U, Direkoglu C et al. On Using Physical Analogies for Feature and Shape Extraction in Computer Vision [J].The Computer Journal,2011,54(1):11-25,
[135]葛玉红.大家学手语[EB/OL].(2008-10-01)[2013-06-28]
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