摘要
本文首先研究了相关性约束运动估值算法,然后提出了基于运动矢量插值的运动估值算法,实验表明新算法的预测性能明显比传统块匹配运动估值算法(BMA)好,而且预测图象的主观质量得到显著改善。
A modified Block Matching Algorithm(BMA) with motion correlation constraint is proposed at first. Then a novel motion estimation algorithm, which computes motion vector for each pixel by interpolating motion vectors is presented. In order to increase interframe prediction gain and decrease the computational complexity, a motion vector optimizing algorithm for local image region is described at last. Experimental results show that the proposed algorithm can improve the prediction performance obviously with a moderately increased complexity compared with the conventional BMA.
基金
国家863项目
自然科学基金(69602003)
博士后基金
