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)


Abstract:




  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.

 



Bio:



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 (akhisti@comm.utoronto.ca)