Abstract: Concerning the high computation complexity of disparity estimation for multiview video encoding, a new method for disparity estimation based on Mean Shift was proposed. The relationship between disparity vector and motion vector in the spatio-temporal domain was analyzed, and the prediction disparity vector was calculated. The initial searching position for disparity matching was confirmed to be the initial value of iteration calculation by Mean Shift, and the macroblock's optimum matching in reference frame was achieved. The experimental results show that the proposed method can save more than 94% encoding time with a negligible drop in rate distortion compared with the full search algorithm. Compared to the fast searching algorithm, this method saves more than 10% encoding time and improves rate distortion.

Key words: disparity estimation, motion vector, Mean Shift, prediction disparity vector, disparity matching

摘要： 针对多视点视频编码视差估计计算量大的特点，提出一种基于均值偏移的多视点视差估计方法。在时空域上，分析了视差矢量和运动矢量的相关性，计算了预测视差矢量，确定了视差匹配的初始搜索位置。将该位置作为均值偏移迭代计算的初值，在参考帧中完成宏块的最佳匹配。通过实验表明：新方法与全搜索算法相比，在率失真性能降低甚微的情况下编码时间降低了94%以上；和快速搜索算法相比，新方法编码时间降低10%以上，率失真性能获得提高。

关键词: 视差估计, 运动矢量, 均值偏移, 预测视差矢量, 视差匹配