Challenges and Opportunities in Video Transmission
Abstract: Supporting video communication over lossy channels such as wireless networks and the Internet is a challenging task due to the stringent quality of service (QoS) required by video applications and the many channel impairments. Two important QoS characteristics for video are the degree of signal distortion and the transmission delay. Another important consideration is the cost associated with transmission, for example, the energy consumption in the wireless channel case and the cost for differentiated services in the Internet (with DiffServ) case.
In this presentation we consider the joint adaptation of the source coding parameters, such as the quantization step-size and prediction mode, along with the physical layer resources, such as the transmission rate and power. Our goal is to provide acceptable QoS while taking into account system constraints such as the energy utilization. We discuss a general framework that allows a number of "resource/distortion" optimal formulations for balancing the requirements of different applications. We conclude the presentation with some of the grand opportunities and challenges in designing and developing video communication systems.
Prof. Aggelos K. Katsaggelos ( IEEE Fellow)
Department of EECS
Northwestern University, USA
Aggelos K. Katsaggelos received the Diploma degree in electrical and mechanical engineering from the
Aristotelian University of Thessaloniki, Greece, in 1979 and the M.S. and Ph.D. degrees both in electrical
engineering from the Georgia Institute of Technology, in 1981 and 1985, respectively. In 1985 he joined the
Department of Electrical Engineering and Computer Science at Northwestern University, where he is currently
professor. He is also the Director of the Motorola Center for Seamless Communications and a member of the
Academic Affiliate Staff, Department of Medicine, at Evanston Hospital.
Dr. Katsaggelos is a member of the Publication Board of the IEEE Proceedings, the IEEE Technical Committees on
Visual Signal Processing and Communications, and Multimedia Signal Processing, the Editorial Board of Academic
Press, Marcel Dekker: Signal Processing Series, Applied Signal Processing, and Computer Journal. He has served as
editor-in-chief of the IEEE Signal Processing Magazine (1997-2002), a member of the Publication Boards of the
IEEE Signal Processing Society, the IEEE TAB Magazine Committee, an Associate editor for the IEEE
Transactions on Signal Processing (1990-1992), an area editor for the journal Graphical Models and Image
Processing (1992-1995), a member of the Steering Committees of the IEEE Transactions on Image Processing
(1992-1997) and the IEEE Transactions on Medical Imaging (1990-1999), a member of the IEEE Technical
Committee on Image and Multi-Dimensional Signal Processing (1992-1998), and a member of the Board of
Governors of the IEEE Signal Processing Society (1999-2001). He is the editor of Digital Image Restoration
(Springer-Verlag 1991), co-author of Rate-Distortion Based Video Compression (Kluwer 1997), co-editor of
Recovery Techniques for Image and Video Compression and Transmission, (Kluwer 1998), and co-author of Super-
Resolution for Images and Video, (Morgan and Claypool, 2007), and co-author of Joint Source-Channel Video
Transmission (Morgan and Claypool 2007). He was the holder of the Ameritech Chair of Information Technology
(1997-2003), and he is the co-inventor of twelve international patents, a Fellow of the IEEE, and the recipient of the
IEEE Third Millennium Medal (2000), the IEEE Signal Processing Society Meritorious Service Award (2001), an
IEEE Signal Processing Society Best Paper Award (2001), and an IEEE ICME Best Paper Award (2006). He is a
Distinguished Lecturer of the IEEE Signal Processing Society for 2007-2008.
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Visual Information Processing for Security Applications
Prof. Tieniu Tan ( IEEE Fellow)
Intelligent Recognition and Digital Security Group
National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences, China
Tieniu Tan graduated with a BSc from Xi'an Jiaotong University in 1984, and obtained his MSc (in 1986) and PhD (in 1989) degrees from Imperial College of Science,Technology and Medicine, London, UK. Prior to his return to China in 1998, He worked at the University of Reading, UK as Research Fellow, Senior Research Fellow and Lecturer. He currently serve as the President of the Institute of Automation as well as the Director of the NLPR. He lead the Intelligent Recognition & Digital Security Group of the NLPR. His current research focuses on the visual surveillance and monitoring of dynamic scenes (for example, the detection and recognition of abnormal behaviors or other specific events), personal identification based on multiple biometric features such as face, iris, fingerprint, handwriting and gait, and watermarking of digital multimedia data such as digital static images and digital video. He also have research projects on image and video databases, invariant visual perception and mobilerobot navigation (especially for intelligent wheelchairs).
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Data Mining Technologies Inspired from Visual Principle
Abstract : In this talk we review the recent work done by our group on data mining (DM) technologies deduced from simulating visual principle. Through viewing a DM problem as a cognition problems and treading a data set as an image with each light point located at a datum position, we developed a series of high efficient algorithms for clustering, classification and regression via mimicking visual principles. In pattern recognition, human eyes seem to possess a singular aptitude to group objects and find important structure in an efficient way. Thus, a DM algorithm simulating visual system may solve some basic problems in DM research. From this point of view, we proposed a new approach for data clustering by modelling the blurring effect of lateral retinal interconnections based on scale space theory. In this approach, as the data image blurs, smaller light blobs merge into large ones until the whole image becomes one light blob at a low enough level of resolution. By identifying each blob with a cluster, the blurring process then generates a family of clusterings along the hierarchy. The proposed approach provides unique solutions to many long standing problems, such as the cluster validity and the sensitivity to initialization problems, in clustering. We extended such an approach to classification and regression problems, through combatively employing the Weber's law in physiology and the cell response classification facts. The resultant classification and regression algorithms are proven to be very efficient and solve the problems of model selection and applicability to huge size of data set in DM technologies. We finally applied the similar idea to the difficult parameter setting problem in support vector machine (SVM). Viewing the parameter setting problem as a recognition problem of choosing a visual scale at which the global and local structures of a data set can be preserved, and the difference between the two structures be maximized in the feature space, we derived a direct parameter setting formula for the Gaussian SVM. The simulations and applications show that the suggested formula significantly outperforms the known model selection methods in terms of efficiency and precision.
The advantages of the proposed approaches are: 1) The derived algorithms are computational stable and insensitive to initialization and they are totally free from solving difficult global optimization problems. 2) They facilitate the construction of new checks on DM validity and provide the final DM result a significant degree of robustness to noise in data and change in scale. 3) They are free from model selection in application. 4) The DM results are highly consistent with those perceived by our human eyes. 5) They provide unified frameworks for scale-related DM algorithms recently derived from many other fields such as estimation theory, recurrent signal processing, information theory and statistical mechanics, and artificial neural networks.
Prof. Zongben Xu
Mathematics and Computer Science
Institute for Information and System Sciences
Xi'an Jiaotong University, China
Zongben Xu received his MS degree in Mathematics in 1981 and PhD degree in applied Mathematics in 1987 from Xi'an Jiaotong University, China. In 1998, he was a post-doctoral researcher in the Department of Mathematics, The University of Strathclyde (UK), He worked as a research fellow in the Department of Computer Science and Engineering from 1992 to 1994, and 1996 to 1997, at The Chinese University of Hong Kong; a visiting professor in the University of Essex (UK) in 2001, and Napoli University (Italy) in 2002. He has been with the Faculty of Science and Institute for Information and System Sciences at Xi`an Jiaotong University since 1982, where he was promoted to associate professor in 1987 and full professor in 1991, and now serves as professor of Mathematics and computer science, director of the Institute for Information and System Sciences, and vice president of Xi'an Jiaotong University. In 2007, he was appointed as a Chief Scientist of National Basic Research Program of China (973 Project).
Professor Xu currently makes several important services for government and professional societies, including Consultant Expert for National (973) Program in Key Basic Science Research and Development (Information group), Ministry of Science and Technology of China; Evaluation Committee Member for Mathematics Degree, Academic Degree Commission of the Chinese Council; Committee Member in Scientific Committee of Education Ministry of China (Mathematics and Physics Group); Vice-Director of the Teaching Guidance Committee for Mathematics and Statistics Majors, the Education Ministry of China; Director of the Teaching Guidance Committee for Mathematics Education, the Education Ministry of China; Member in the Expert Evaluation Committee for Natural Science Foundation of China (Computer Science Group), The National Committee for Natural Science Foundation of China; Vice-president of Computational Intelligence Society of China; Editor-in-chief of the Textbooks on Information and Computational Sciences, Higher Education Press of China; Co-editor of nine national and international journals.
Professor Xu has published over 150 academic papers on non-linear functional analysis, optimization techniques, neural networks, evolutionary computation, and data mining algorithms, most of which are in international journals. His current research interests include non-linear analysis, machine learning and computational intelligence. Dr. Xu holds the title "Owner of Chinese PhD Degree Having Outstanding Achievements" awarded by the Chinese State Education Commission (CSEC) and the Academic Degree Commission of the Chinese Council in 1991. He is owner of the National Natural Science Award of China in 2007.
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