- Research Article
- Open Access
Subgraphs Matching-Based Side Information Generation for Distributed Multiview Video Coding
EURASIP Journal on Advances in Signal Processing volume 2009, Article number: 386795 (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.
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Xiong, H., Lv, H., Zhang, Y. et al. Subgraphs Matching-Based Side Information Generation for Distributed Multiview Video Coding. EURASIP J. Adv. Signal Process. 2009, 386795 (2010). https://doi.org/10.1155/2009/386795
- Side Information
- Thin Plate Spline
- Scale Invariant Feature
- Descriptor Sift
- Sparse Decomposition