中文摘要：

运动估计是去除视频时间维冗余的编码技术，而目前通用的平移运动模型无法有效地表示物体的局部非刚性复杂运动．为此，提出一种基于改进高斯．牛顿法的弹性运动估计方法．首先，通过分析初始迭代点对高斯．牛顿迭代结果的影响，采用基于2bit深度像素的均匀搜索预测初始迭代点；其次，通过理论和实验分析发现，不同的迭代步长对弹性运动估计／补偿性能有明显的影响，采用离散余弦变换的低频能量比率估计步长的上限，再利用黄金分割法对步长进行求精．实验结果表明，对于具有不同场景特点的视频序列，该算法始终能够保持较高的估计精度，运动补偿的平均峰值信噪比，比基于块平移模型的全搜索算法和传统弹性运动估计算法分别提高1．73dB和1．42dB．并且，该算法具有更快的收敛速度，一般仅需1—3次迭代就能取得高于传统弹性运动估计和块平移全搜索的峰值信噪比．

英文摘要：

Motion estimation is a coding technique to eliminate the temporal redundancy of video. However, state-of-the-art translational motion model is not able to efficiently represent objects＇ local non-rigid complex motion. To address the issue, an elastic motion estimation algorithm is developed in this paper based on modified Gauss-Newton method. The effect of initial iteration point is first analyzed on the result of the Gauss-Newton method, and a two bit-depth pixel based uniform search is used to predict the initial iteration point. Subsequently, it is found that different step size has obvious influence on the performance of the elastic motion estimation by both theoretical and experimental analyses. The ratio of low-frequency energy of discrete cosine transform is employed to estimate the upper bound of the step size which is then refined by the golden ratio method. Experimental results show that the proposed algorithm is able to obtain stable performance for video sequences with various scene characteristics. It gains 1.73dB and 1.42dB higher average motion-compensated peak signal-to-noise ratio （PSNR） than those of the full search algorithm based on block-wise translational model and conventional elastic motion estimation, respectively. Furthermore, the proposed algorithm has faster convergence speed. Only 1~3 iterations are needed before the proposed algorithm achieves higher PSNR than conventional elastic motion estimation and block-wise translational full search method.