【摘要】 提出了一种2 bit深度像素的运动估计算法。首先,将像素深度的降采样过程形式化为区间分划和区间映射2个步骤,其中前者为多对一映射,决定着运动估计性能,后者为一一映射;其次,提出一种非均匀量化方法求解区间分划的3个初始阈值,并利用隶属度函数对初始阈值细化,从而克服信号噪声等因素导致的初始阈值周围像素值的误匹配;再次,讨论了适用于2 bit深度像素运动估计的误差度量准则,进而提出了基于模糊量化和2 bit深度像素的运动估计算法;最后,借助信号自相关函数,建立比特深度转换误差—运动向量精度模型来估计该算法所能达到的预测精度。实验结果证明,对于多种类型的视频序列,尤其是场景细节和物体运动比较复杂者,该算法始终能保持较高的估计精度,运动补偿的平均峰值信噪比较之传统2 bit深度像素的运动估计提高0.27 dB。更多还原

【Abstract】 A motion estimation algorithm was proposed using 2 bit-depth pixels.The reduction of pixel depth was first formalized by two successive steps,namely interval partitioning and interval mapping.The former is a many-to-one mapping which determines motion estimation performance,while the latter is a one-to-one mapping.A non-uniform quantization method was then presented to compute three initial thresholds of the interval partitioning.These initial thresholds were subsequently refined by using a membership function to solve the mismatch of pixel values near them caused by signal noise and so on.Afterwards,a matching criterion was discussed suitable for the motion estimation using 2 bit-depth pixels.A novel motion estimation algorithm was consequently addressed based on 2 bit-depth pixels and fuzzy quantization.To further predict the precision of the proposed algorithm,a bit resolution reduction error-motion vector precision model was built by exploiting the auto-correlation function.Extensive experimental results show that the proposed algorithm can always achieve high motion estimation precision for video sequences with various characteristics,especially for those with detailed scene and complex motion.Compared with traditional 2 bit motion estimation,the proposed algorithm gains 0.27 dB improvement in terms of average peak signal-to-noise ratio of motion compensation.更多还原