earticle

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.