Conventional Fast Motion Estimation Algorithms Research Articles

Pattern Based Motion Estimation using Zero Motion Pre-judgement and Quantum behaved Particle Swarm Optimization

Motion estimation is a fundamental and resource hungry operation in most of the video coding applications. The most popular method used in any video coding application is block matching motion estimation (BMME). This conventional fast motion estimation algorithm adopts a monotonic error surface for faster computation. However, these search techniques may trap at local minima resulting in erroneous motion estimation. To overcome this issue, various evolutionary swarm intelligence based algorithms were proposed. In this paper, a pattern based motion estimation using zero motion prejudgment and Quantum behaved Particle Swarm Optimization (QPSO) algorithms is proposed, referred to as the Pattern Based Motion Estimation (PBME) algorithm. The notion of QPSO improves the diversity in the search space, which enhances the search efficiency and helps in reduction of the computational burden. At the same time, QPSO needs fewer parameters to control. Therefore, the proposed algorithm enhances the estimation accuracy. An initial search pattern (Hexagonal Based Search) was used which speeds the convergence rate of the algorithm. From the simulation results, it was found that the proposed method outperformed the existing fast block matching (BMA) algorithms of the search point reduction by 40–75%

Adaptive direction search algorithms based on motion correlation for block motion estimation

The new video coding standard, H.264/AVC, has achieved significant compression gain by utilizing several advanced techniques. Block-Matching Motion Estimation is one of the most important elements to reduce the motion residual. However, it results in heavy computational burden and limits the application for real-time video service. In this paper, a fast motion estimation algorithm called Simulated Annealing Adaptive Search (SAAS) is proposed to reduce the computational load. The basic idea of the proposed scheme is based on adjusting search pattern not only for each frame, but also for each block. Initially, the adaptive search pattern is performed by statistical analysis of previous frame's Motion Vector Correlation. Then the search pattern is adjusted for each block according to Predicted Motion Vector. According to motion vector correlation statistics information, search region is adaptively divided and Simulated Annealing (SA) mechanism is adopted to select search power for each region and to avoid trapping into local minima. Experimental results indicate that the proposed algorithm offers considerable improvement in computing time and motion search points at the same rate-distortion performance, compared to the conventional fast motion estimation algorithm.

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

PDF HTML阅读 XML下载 导出引用 引用提醒 二值alpha平面辅助的视频对象快速运动估计算法 DOI: 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: Supported by the National Natural Science Foundation of China under Grant Nos.60372071, 60703084, 60723003 (国家自然科学基金); the Natural Science Foundation of Liaoning Province of China under Grant No.20032105 (辽宁省自然科学基金); the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2007571 (江苏省自然科学基金); the Project Innovation of Graduate Students of Jiangsu Province of China under Grant No.CX07B-121Z (江苏省普通高校研究生科研创新计划); the Plan for Supporting the Colleges' Outstanding Talented Person of Liaoning Province of China under Grant No.RC-04-11 (辽宁省高等学校优秀人才支持计划) Binary Alpha-Plane Assisted Fast Motion Estimation Scheme of Video Object Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要: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(diamond search)和PSA(priority search algorithm)(BAAS(binary alpha-plane assisted search)+DS)高出0.1dB~ 0.8dB,略低于FS(full search),但是其计算复杂度与FS相比降低了20倍. Abstract: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. 参考文献 相似文献 引证文献