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    Rate Control of H.264/SVC for Joint Temporal-Quality Scalability
    Update time: 2009-05-18
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    Speaker:Prof. C.-C. Jay Kuo  University of Southern California
    Time:3:00pm-4:00pm, May.19, 2009 (TUE)
    Place:Report Hall,4th Floor,ICT

      The scalability of coded video has drawn a lot of attention in the literature due to its potential advantages in offering a flexible video transmission solution in a heterogeneous networking environment. H.264/SVC has recently been standardized as a scalable extension of the H.264/AVC video coding standard. To address the bit allocation problem for H.264/SVC, we study dependent rate and distortion (R-D) models for temporal and quality scalabilities of H.264/SVC. Unlike previous models, we examine dependent R-D characteristics based on the self-domain analysis (namely, R- and D-domain analysis for the rate and the distortion characteristics, respectively), where the R-D characteristics of a dependent coding unit are expressed as the R-D characteristics of its reference and/or base layers. With the proposed R-D models, the complex dependent R-D characteristics can be simplified to be a linear sum of functions of a single parameter. As a result, the bit allocation problem can be solved elegantly. We conduct studies on two bit allocation algorithms, which are formulated as the Lagrangian optimization problem. First, we investigate a temporal layer bit allocation problem based on its dependent distortion model. Then, we examine the joint quality-temporal layer bit allocation problem by combining temporal and quality layer R-D models. One important advantage of the proposed R-D models is that they allow an analytical solution to the Lagrange equation. With the proposed algorithms, both bit allocation problems are numerically solved at significantly reduced complexity. It is shown by experimental results that the proposed algorithms could produce more efficient scalable bit streams than those by the H.264/SVC reference software codec (JSVM) at various bit rates with different types of test sequences.

      Dr. C.-C. Jay Kuo received the Ph.D. degrees from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of Signal and Image Processing Institute and Professor of EE, CS and Mathematics. His research interests are in the areas of digital media processing, multimedia compression, communication and networking technologies, and embedded multimedia system design. Dr. Kuo is a Fellow of IEEE and SPIE. Dr. Kuo has guided about 90 students to their Ph.D. degrees and supervised 20 postdoctoral research fellows. Currently, his research group at USC consists of around 35 Ph.D. students (see website http://viola.usc.edu), which is one of the largest academic research groups in multimedia technologies. He is a co-author of about 150 journal papers, 780 conference papers and 9 books. Dr. Kuo is Editor-in-Chief for the Journal of Visual Communication and Image Representation, and Editor for the Journal of Information Science and Engineering, LNCS Transactions on Data Hiding and Multimedia Security (a Springer journal), the Journal of Advances in Multimedia (a Hindawi journal) and the EURASIP Journal of Applied Signal Processing (a Hindawi journal). He was on the Editorial Board of the IEEE Signal Processing Magazine in 2003-2004. He served as Associate Editor for IEEE Transactions on Image Processing in 1995-98, IEEE Transactions on Circuits and Systems for Video Technology in 1995-1997 and IEEE Transactions on Speech and Audio Processing in 2001-2003.


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