二值alpha平面辅助的视频对象快速运动估计算法

提出了一种任意形状视频对象的快速运动估计方法.详细分析了alpha平面在视频对象的快速运动估计过程中起到的指导性作用,采用边界扩展和边界掩码技术,提出了一种新的二值alpha平面匹配衡量准则WBAMC (weighted binary alpha-plane matching criterion).结合优先搜索策略,提出了二值alpha平面辅助的视频对象快速运动估计算法BAAME(binary alpha-plane assisted motion estimation),.首先,利用alpha平面和WBAMC准则,将边界宏块的搜索范围缩小至两个搜索起点的单调区域,再采用传统的快速运动估计算法确定其运动向量;然后,用边界宏块的运动向量预测内部宏块的搜索起点;最后,采用快速运动估计算法搜索内部宏块的运动向量.这种方法可与多种空间域和频率域运动估计算法相结合,有效地应用于基于对象的视频编码器中.实验结果表明,对于多种类型的标准测试视频流,BAAME算法始终能够保持较高的估计精度和主观质量,运动补偿的平均PSNR(peak signal-to-noise ratio)较DS(diamosd search)和PSA(pdoety search algorithm)(BAAS(binary alpha-plant assisted search) DS)高出0.1dB～0.SdB,略低于FS(full search),但是其计算复杂度与FS相比降低了20倍.

Binary Alpha-Plane Assisted Fast Motion Estimation Scheme of Video Object

This paper proposes a fast motion estimation scheme of arbitrarily shaped video object. The instructive role of the alpha-plane taking part in the motion estimation of video object is discussed, and a weighted binary alpha-plane matching criterion (WBAMC) is proposed by using boundary extension and boundary mask techniques. Based on priority search strategy, this paper proposes a fast binary alpha-plane assisted motion estimation (BAAME) scheme of video object. First, the BAAME uses alpha-plane and the WBAMC criterion to limit the search of boundary macro-blocks (MBs) into small unimodal area of two starting points so that a conventional fast motion estimation algorithm can be employed to search the motion vectors (MVs) of boundary MBs. Second, the BAAME predicts the starting points of opaque MBs by using MVs of neighboring boundary MBs and then employs a fast motion estimation algorithm to compute the MVs of opaque MBs. The proposed scheme can be combined with many spatial domain based and frequency domain based different motion estimation algorithms, and be effectively applied to object-based video codecs. The experimental results show that the BAAME scheme can always reach high motion estimation accuracy and better subjective quality for standard test video sequences which have different characteristics respectively. The proposed scheme can achieve 0.1~0.8dB higher prediction quality on average than DS (diamond search) and PSA (priority search algorithm) (BAAS (binary alpha-plane assisted search)+DS), and a little lower PSNR (peak signal-to-noise ratio) than FS (full search). Moreover, the BAAME scheme can speed up the motion estimation about 20 times in terms of computational complexity when compared with FS.