Open Access

Distributed Coding of Highly Correlated Image Sequences with Motion-Compensated Temporal Wavelets

EURASIP Journal on Advances in Signal Processing20062006:046747

https://doi.org/10.1155/ASP/2006/46747

Received: 21 March 2005

Accepted: 4 October 2005

Published: 12 March 2006

Abstract

This paper discusses robust coding of visual content for a distributed multimedia system. The system encodes independently two correlated video signals and reconstructs them jointly at a central decoder. The video signals are captured from a dynamic scene, where each signal is robustly coded by a motion-compensated Haar wavelet. The efficiency of the decoder is improved by a disparity analysis of the first image pair of the sequences, followed by disparity compensation of the remaining images of one sequence. We investigate how this scene analysis at the decoder can improve the coding efficiency. At the decoder, one video signal is used as side information to decode efficiently the second video signal. Additional bitrate savings can be obtained with disparity compensation at the decoder. Further, we address the theoretical problem of distributed coding of video signals in the presence of correlated video side information. We utilize a motion-compensated spatiotemporal transform to decorrelate each video signal. For certain assumptions, the optimal motion-compensated spatiotemporal transform for video coding with video side information at high rates is derived. It is shown that the motion-compensated Haar wavelet belongs to this class of transforms. Given the correlation of the video side information, the theoretical bitrate reduction for the distributed coding scheme is investigated. Interestingly, the efficiency of multiview side information is dependent on the level of temporal decorrelation: for a given correlation SNR of the side information, bitrate savings due to side information are decreasing with improved temporal decorrelation.

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Authors’ Affiliations

(1)
Max Planck Center for Visual Computing and Communication, Stanford University
(2)
Signal Processing Institute, Swiss Federal Institute of Technology Lausanne

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Copyright

© Flierl and Vandergheynst 2006