Subgraphs Matching-Based Side Information Generation for Distributed Multiview Video Coding. (22nd March 2010)

• Record Type: Journal Article
• Authors: Xiong Xiong, Hongkai Hongkai; Lv Lv, Hui Hui; Zhang Zhang, Yongsheng Yongsheng; Song Song, Li Li; He He, Zhihai Zhihai; Chen Chen, Tsuhan Tsuhan
• Other Names: Chen Chen Mei-Juan Mei-Juan Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2009(2009)
• Publication Date: 2010-03-22
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2009/386795
• Languages: English
• ISSNs: 1687-6172

Abstract

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.