Toronto Networking Seminar

Organized by Department of Computer Science and Department of Electrical and Computer Engineering, University of Toronto

Adaptive Distributed Source Coding

David Varodayan
Department of Electrical Engineering
Stanford University


Friday, April 30, 2pm
Location: BAB024 (Bahen Centre Basement)


Distributed source coding is a type of data compression that arises naturally in networks, ranging from peer-to-peer systems to camera arrays. The principle is to exploit the correlation of multiple sources of data at a joint decoder, so that the encoders can compress efficiently without communicating with each other. This is well-suited
to network settings in which communication among encoders comes at a cost.

In this talk, we introduce novel algorithms to address the unpredictable statistics of nonergodic image and video signals. At the encoding side, our invention of rate-adaptive distributed low-density parity-check codes lets the encoders switch among encoding rates flexibly. When these codes are combined with feedback from the decoder, the encoders can be agnostic of statistics. At the decoding side, we propose an iterative unsupervised learning algorithm to estimate statistical parameters that relate the multiple sources. These techniques make distributed source coding much more versatile for real signals. We then apply these algorithms to a variety of networked media systems: multiview image coding for camera arrays, low-complexity video encoding, image authentication for insecure distribution networks, and quality monitoring of streamed video.


David Varodayan received the Ph.D. in Electrical Engineering at Stanford University in 2010. He also holds a B.A.Sc. in Engineering Science from the University of Toronto, and an M.S. in Electrical Engineering from Stanford. His research interests are in the areas of data compression and machine learning, applied particularly to video coding and media security. He has won Best Student Paper Award on two occasions and the Signal Processing Journal's Most Cited Paper Award in 2009. In June 2010, he will begin at post-doc at HP Labs in Palo Alto.

Host of Talk:

Ashish Khisti (