ICICS 2015

10th International Conference on Information, Communications and Signal Processing

December 2-4, 2015


C.-C. Jay KUO  
  Reflection on Image/Video Coding: Where Do We Go from Here?
  Dr. C.-C. Jay Kuo
  University of Southern California
Abstract: Image/video coding has been an active research field for almost half a century. Many technologies were developed and quite a few important standards such as JPEG, MPEG, H.264 and H.265 have great impact on our daily life. Apparently, the image/video coding research field has reached a certain level of maturity, and the question “is there anything left for video coding?” arises. One promising R&D direction is “perceptual coding”. That is, we may leverage the characteristics of the human visual system (HVS) to achieve a higher coding gain. To achieve this goal, we need to change the traditional quality/distortion measure (i.e., PSNR/MSE) to a new perceptual quality/distortion measure. In this talk, I will present a completely new approach to address this problem. This approach is driven by a fundamental question - “How many distinct quality levels can be perceived by most people?” When we attempt to answer this question, it becomes clear that the traditional R-D optimization framework commonly used in image/video coding may not be suitable for perceptual coding and a new framework based on the notion of just noticeable differences (JND) and the machine learning methodology is needed. This new research direction is expected to open a brand new page of image/video coding.

Biography: Dr. C.-C. Jay Kuo received the Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Dean’s Professor of EE, CS and Mathematics. His research interests are in the areas of multimedia technologies and computer vision. Currently, his research laboratory at USC has around 30 Ph.D. students (see website http://mcl.usc.edu), which is one of the largest academic research groups in multimedia technologies. Dr. Kuo has guided 126 students to their Ph.D. degrees and supervised 25 postdoctoral research fellows. He is a co-author of about 230 journal papers, 870 conference papers and 13 books. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE.

Dr. Kuo received the National Science Foundation Young Investigator Award (NYI) and Presidential Faculty Fellow (PFF) Award in 1992 and 1993, respectively. He was an IEEE Signal Processing Society Distinguished Lecturer in 2006, and the recipient of the Electronic Imaging Scientist of the Year Award in 2010 and the holder of the 2010-2011 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies. He was a recipient of the Pan Wen-Yuan Outstanding Research Award in 2011, and the recipient of the 2014 Northrop Grumman Excellence in Teaching Award. He was President of Asia-Pacific Signal and Information Processing Association (APSIPA) in 2013-2014.
Kin K. LEUNG  
  From Distributed Optimization Theory to In-Network Data Processing in Wireless Ad-Hoc and Sensor Networks
  Dr. Kin K. Leung
  Imperial College
Abstract: In this talk, the speaker will begin with a brief overview of distributed optimization theory, including convex optimization problems for which distributed, iterative solution techniques exist and converge. As for wireless ad-hoc and sensor networks, it is well known that each link capacity in these networks depends on the transmission power of other links. In addition, the quality of multimedia services supported by these networks cannot be represented by a concave function of the amount of allocated bandwidth. These factors unfortunately make the resource allocation problem for the wireless networks become a non-convex optimization problem. New distributed solution techniques will be presented to solve these problems and numerical examples will also be provided.

As the second part of this talk, the speaker considers the in-network data processing in wireless sensor networks where data are aggregated (fused) along the way they are transferred toward the end user. It will be shown that finding the optimal solution for the distributed processing problem is NP-hard, but for specific parameter settings, the problem can lead to a distributed framework for the global optimal solution. Future work on integrating data or signal processing techniques with the distributed solution framework will be discussed.

Biography: Kin K. Leung received his B.S. degree from the Chinese University of Hong Kong in 1980, and his M.S. and Ph.D. degrees from University of California, Los Angeles, in 1982 and 1985, respectively. He joined AT&T Bell Labs in New Jersey in 1986 and worked at its successor companies, AT&T Labs and Bell Labs of Lucent Technologies, until 2004. Since then, he has been the Tanaka Chair Professor in the Electrical and Electronic Engineering (EEE), and Computing Departments at Imperial College in London. He serves as the Head of Communications and Signal Processing Group in the EEE Department at Imperial. His research focuses on networking, protocols, optimization and modeling issues of wireless broadband, sensor and ad-hoc networks. He also works on multi-antenna systems and cross-layer optimization of these networks.

He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs in 1994, and was a co-recipient of the 1997 Lanchester Prize Honorable Mention Award. He was elected as an IEEE Fellow in 2001. He received the Royal Society Wolfson Research Merits Award from 2004 to 2009 and became a member of Academia Europaea in 2012.

Along with his co-authors, he also received a number of best paper awards at major conferences, including the IEEE PIMRC 2012 and ICDCS 2013. He serves as a member (2009-11) and the chairman (2012-15) of the IEEE Fellow Evaluation Committee for Communications Society. He was a guest editor for the IEEE JSAC, IEEE Wireless Communications and the MONET journal, and as an editor for the JSAC: Wireless Series, IEEE Transactions on Wireless Communications and IEEE Transactions on Communications. Currently, he is an editor for the ACM Computing Survey and International Journal on Sensor Networks.