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Multi-Description Video Streaming with Optimized Reconstrution Based DCT and Neural-Network Compensations
IEEE Transactions on Multimedia (2001)
  • Xiao Su, San Jose State University
  • Benjamin W. Wah, University of Illinois at Urbana-Champaign

Packet and compression losses are two sources of quality losses when streaming compressed video over unreliable IP networks, such as the Internet. In this paper, we propose two new approaches for concealing such losses. First, we present a joint sender-receiver approach for designing transforms in multidescription coding (MDC). In the receiver, we use a simple interpolation-based reconstruction algorithm, as sophisticated concealment techniques cannot be employed in real time. In the sender we design an optimized reconstruction-based discrete cosine transform (ORB-DCT) with an objective of minimizing the mean squared error, assuming that some of the descriptions are lost and that the missing information is reconstructed by simple averaging at the destination. Second, we propose artificial neural network to compensate for compression losses introduced in MDC. Experimental results show that our proposed algorithms perform well in real internet tests.

  • video streaming,
  • DCT,
  • optimized
Publication Date
March, 2001
Publisher Statement
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Citation Information
Xiao Su and Benjamin W. Wah. "Multi-Description Video Streaming with Optimized Reconstrution Based DCT and Neural-Network Compensations" IEEE Transactions on Multimedia Vol. 3 Iss. 1 (2001)
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