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Open Access Research Article

Subgraphs Matching-Based Side Information Generation for Distributed Multiview Video Coding

Hongkai Xiong12*, Hui Lv1, Yongsheng Zhang1, Li Song1, Zhihai He3 and Tsuhan Chen2

Author Affiliations

1 Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

2 Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA

3 Department of Electrical and Computer Engineering, University of Missouri-Columbia, MO 65211, USA

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EURASIP Journal on Advances in Signal Processing 2009, 2009:386795  doi:10.1155/2009/386795

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/386795


Received:23 April 2009
Revisions received:25 September 2009
Accepted:9 December 2009
Published:22 March 2010

© 2009 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We adopt constrained relaxation for distributed multiview video coding (DMVC). The novel framework integrates the graph-based segmentation and matching to generate interview correlated side information without knowing the camera parameters, inspired by subgraph semantics and sparse decomposition of high-dimensional scale invariant feature data. The sparse data as a good hypothesis space aim for a best matching optimization of interview side information with compact syndromes, from inferred relaxed coset. The plausible filling-in from a priori feature constraints between neighboring views could reinforce a promising compensation to interview side-information generation for joint multiview decoding. The graph-based representations of multiview images are adopted as constrained relaxation, which assists the interview correlation matching for subgraph semantics of the original Wyner-Ziv image by the graph-based image segmentation and the associated scale invariant feature detector MSER (maximally stable extremal regions) and descriptor SIFT (scale-invariant feature transform). In order to find a distinctive feature matching with a more stable approximation, linear (PCA-SIFT) and nonlinear projections (Locally linear embedding) are adopted to reduce the dimension SIFT descriptors, and TPS (thin plate spline) warping model is to catch a more accurate interview motion model. The experimental results validate the high-estimation precision and the rate-distortion improvements.

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