Earticle

초록

영어

In this work, we have developed a new bio-inspired neural network algorithms for block-based motion estimation. The main goal is to bridge the gap between algorithmic and biological vision by suggesting a bio-inspired motion estimation model based on neural network. We simplify the matching criterion for the block matching algorithm to reduce the hardware complexity and a number of input ports which maintaining the good quality. This paper implements the optimized algorithm in the reference model of H.264 compiled by VC6.0, and chooses six typical video sequences for simulation. The results show that our algorithm can reduce the average search points up to 82% to the full search black-matching algorithm. The optimized algorithm has reduced the motion estimation by 13.792% compared with UMHexagonS, and it gets better optimization to video testing sequences with low complexity.

목차

Abstract
 1. Introduction
 2. Bio-inspired neural networks
  2.1. The Structure
  2.2. The transform-domain transcoding
 3. The Algorithm
 4. Simulation Results
 5. Conclusions and future work
 Acknowledgments
 References

키워드

저자

• Youwei Yuan [ School of Computer Science and Technology, Hangzhou Dianzi University,Hangzhou, Zhejiang, P.R China, 310018 ]
• Weilei Xu [ School of Computer Science and Technology, Hangzhou Dianzi University,Hangzhou, Zhejiang, P.R China, 310018 ]
• Yong Li [ School of Computer Science and Technology, Hangzhou Dianzi University,Hangzhou, Zhejiang, P.R China, 310018 ]
• Lamei Yan [ School of Media and Design, Hangzhou Dianzi University,Hangzhou, Zhejiang, P.R China, 310018 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605