摘要
为了提高视频编码效率,提出一种基于多极小值粒子群的快速运动估计算法.该算法将运动矢量特性和多极小值粒子群算法的全局搜索特性结合,采用自适应运动强度、运动矢量预测以及提前终止迭代等方法,克服单峰误差曲面假设的限制.实验结果表明,对运动平缓和中等的视频序列,该算法的运算复杂度与DS相当.对于运动剧烈的视频序列,该算法的运算复杂度与TSS相当.在增加少量搜索点数的情况下,各类视频序列的搜索精度都接近FS.
A fast motion estimation algorithm based on multi-miminum particle swarm optimization was proposed to improve the video coding efficiency. By integrating the characteristic of motion vector with the global searching of MMPSO and using strategies like adaptive motion intensity,motion vector prediction and early termination criteria of iteration,the proposed algorithm overcomed the restriction of assumption about the unimodal error curved surface. The experimental results showed that the computational complexity of algorithm was similar to the DS for the video sequences with slow and middle motion. For the video sequences with violent motion,the computational complexity of algorithm was close to the TSS. In the case of increasing a few search points,the search accuracy was close to the FS for all kinds of video sequences.
出处
《湖北大学学报(自然科学版)》
CAS
2015年第2期174-178,共5页
Journal of Hubei University:Natural Science
基金
国家自然科学基金(51171061)资助
关键词
多极小值
块匹配算法
粒子群优化算法
自适应
运动估计
multiple-minimum
block matching algorithm
particle swarm optimization
adaptive
motion estimation