Graph matching based side information generation for distributed multi-view video coding

  • Authors:

  • Hui Lv;Hongkai Xiong;Li Song;Zhihai He;Tsuhan Chen

  • Affiliations:

  • Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China and Dept. of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Electrical and Computer Engineering, University of Missouri-Columbia, MO;Dept. of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • ICC'09 Proceedings of the 2009 IEEE international conference on Communications
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we adopt constrained relaxation for distributed multi-view video coding (DMVC). The novel framework integrates the graph-based segmentation and matching to generate inter-view correlated side information without knowing the camera parameters. Moreover, graph-based representations of multi-view images are incorporated to form more distinctive feature constraints. The sparse data as a good hypothesis space aim for a best matching optimization of inter-view 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 inter-view side information generation for joint multi-view decoding. In order to find distinctive feature matching with a more stable approximation, PCA-SIFT and TPS (thin plate spline) are adopted to reduce the dimension of SIFT descriptors and construct a more accurate inter-view motion model. The experimental results validate the high estimation precision and the rate-distortion improvements.