Subgraphs Matching-Based Side Information Generation for Distributed Multiview Video Coding
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
EURASIP Journal on Advances in Signal Processing 2009, 2009:386795 doi:10.1155/2009/386795Published: 22 March 2010
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.