Fast motion and disparity estimation for multiview video coding

  • Authors:

  • Zhipin Deng;Kebin Jia;Yui-Lam Chan;Chang-Hong Fu;Wan-Chi Siu

  • Affiliations:

  • Department of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China 100124;Department of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China 100124;Department of Electronic Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China;Department of Electronic Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China;Department of Electronic Information Engineering, The Hong Kong Polytechnic University, Hong Kong, China

  • Venue:
  • Frontiers of Computer Science in China
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Multiview video involves a huge amount of data, and as such, efficiently encoding each view is a critical issue for its wider application. In this paper, a fast motion and disparity estimation algorithm is proposed, utilizing the close correlation between temporal and interview reference frames. First, a reliable predictor is found according to the correlation of motion and disparity vectors. Second, an iterative search process is carried out to find the optimal motion and disparity vectors. The proposed algorithm makes use of the prediction vector obtained in the previous motion estimation for the next disparity estimation and achieves both optimal motion and disparity vectors jointly. Experimental results demonstrate that the proposed algorithm can successfully save an average of 86% of computational time with a negligible quality drop when compared to the joint multiview video model (JMVM) full search algorithm. Furthermore, in comparison with the conventional simulcast coding, the proposed algorithm enhances the video quality and also greatly increases coding speed.