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

We adopt constrained relaxation for distributed multiview video coding (DMVC). The novel framework integrates the graphbased 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.