【摘要】 提出一种用2b深度的像素预测搜索起点的快速小波域运动估计算法.首先,将像素深度的转换形式化为区间分划和区间映射,采用非均匀量化求解区间分划的初始阈值,再用隶属函数计算量化阈值并完成区间映射,从而获得位深度为2b的视频表示;其次,设计了非均匀的搜索起点分布模板,并以此为基础提出一种基于2b深度像素的搜索起点预测算法;最后,以搜索起点为中心,进一步采用改进的低频子带平移运动估计算法MLBSSME在较小的窗口内完成搜索.实验结果表明,对于具有不同场景特点的视频序列,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低频子带平移运动估计和直接子带运动估计算法高0.41dB和1.43dB,比空间域全搜索降低0.07dB.但是,算法的计算量仅相当于空间域全搜索的4.66%、低频子带平移运动估计的4.62%、子带直接运动估计的22.70%.更多还原

【Abstract】 This paper proposes a fast wavelet-domain motion estimation algorithm with initial search point prediction using pixels of two-bit depth. Firstly, we formalize the procedure of bit-depth transformation by two successive steps, namely interval partitioning and interval mapping. A non-uniform quantization method is employed to compute three coarse thresholds. Then they are refined by using a membership function so as to obtain a video representation whose pixels are two-bit depth. Secondly, we design a non-uniform search point pattern and subsequently put forward an initial search point prediction algorithm based on two-bit depth presentation. Finally, centered on the predicted initial search point, the modified low-band-shift-based scalable video motion estimation (MLBSSME) is accepted to search motion vectors in a size-reduced window. Experimental results illustrate that the proposed algorithm can always achieve high motion estimation precision for video sequences with various characteristics. Our algorithm separately gains 0.41 dB and 1.43 dB higher average peak signal-to-noise ratio (PSNR) than the low-band-shift and band-to-band motion estimations, but 0.07 dB lower than full search (FS) in spatial domain. Nevertheless, the computational complexity of our algorithm is about 4.66% of FS, 4.62% of the low-band-shift motion estimation, and 22.70% of the band-to-band motion estimation.更多还原