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Proceedings Paper

Fully scalable video transmission using the SSM adaptation framework
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Paper Abstract

Recently a methodology for representation and adaptation of arbitrary scalable bit-streams in a fully content non-specific manner has been proposed on the basis of a universal model for all scalable bit-streams called Scalable Structured Meta-formats (SSM). According to this model, elementary scalable bit-streams are naturally organized in a symmetric multi-dimensional logical structure. The model parameters for a specific bit-stream along with information guiding decision-making among possible adaptation choices are represented in a binary or XML descriptor to accompany the bit-stream flowing downstream. The capabilities and preferences of receiving terminals flow upstream and are also specified in binary or XML form to represent constraints that guide adaptation. By interpreting the descriptor and the constraint specifications, a universal adaptation engine sitting on a network node can adapt the content appropriately to suit the specified needs and preferences of recipients, without knowledge of the specifics of the content, its encoding and/or encryption. In this framework, different adaptation infrastructures are no longer needed for different types of scalable media. In this work, we show how this framework can be used to adapt fully scalable video bit-streams, specifically ones obtained by the fully scalable MC-EZBC video coding system. MC-EZBC uses a 3-D subband/wavelet transform that exploits correlation by filtering along motion trajectories, to obtain a 3-dimensional scalable bit-stream combining temporal, spatial and SNR scalability in a compact bit-stream. Several adaptation use cases are presented to demonstrate the flexibility and advantages of a fully scalable video bit-stream when used in conjunction with a network adaptation engine for transmission.

Paper Details

Date Published: 23 June 2003
PDF: 18 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.509874
Show Author Affiliations
Debargha Mukherjee, Hewlett-Packard Labs. (United States)
Peisong Chen, Rensselaer Polytechnic Institute (United States)
Shih-Ta Hsiang, Hewlett-Packard Labs. (United States)
John W. Woods, Rensselaer Polytechnic Institute (United States)
Amir Said, Hewlett-Packard Labs. (United States)

Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

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