基于差分的块匹配运动估计算法

通过研究块匹配算法当中菱形算法中的冗余性,提出了一种基于差分的块匹配快速搜索算法（DBS）,利用搜索点的匹配误差之间的大小关系,预测最优点的搜索方向,减少了搜索点数,通过实验表明了该算法在保证了匹配精度的同时获得了更高的搜索速度。     

基于空间关系的改进菱形块匹配策略

研究运动估计(ME)中的经典块匹配算法(BMA)并重点分析菱形(DS)算法,然后利用相邻块之间空间关系,设计一种新的块匹配策略,以相邻宏块的搜索结果进行预测匹配,以减少搜索点数。最后以平均每块搜索点数和平均峰值信噪比为衡量指标验证了该算法的有效性。     

快速运动估计中一种改进的块匹配免疫算法

图像的运动估计中,基于块匹配的免疫算法由于算法的随机性,使得抗体群中不匹配块增多,导致了免疫算法运算量增大。改进算法搜索过程中,通过快速识别出搜索窗内不可能成为匹配块的候选块,并把这些块消除掉,不对其进行匹配误差运算,从而可以大幅度减少免疫算法运算量。同时,由于改进算法使得候选块的匹配可能性提高,使得相对于传统块匹配免疫算法,重建图像的均峰信噪比进一步提高,进而重建图像质量得以提高。仿真实验结果验证了改进算法的上述优点。     

基于多向小十字块模板的块匹配运动估计算法

在研究和分析图像压缩中块匹配运动估计算法的基础上,提出了一种采用多向小十字模板进行快速块匹配运动估计算法MP-SDSP,算法根据序列图像中运动矢量场中心偏置分布特性,利用一条以上的路径寻找最优运动矢量,避免了在快速搜索中陷入错误路径。试验结果证明,在搜索精度基本相同的情况下,MP-SDSP算法能有效减少搜索点数,提高搜索速度,并适应小运动块和大运动块的搜索。     

一种改进的药代动力学参数优化方法及其应用

基于药代动力学参数优化方法PKAIN人工免疫网络算法,提出了迭代分组并发单纯形算子,并实现了线性网络抑制函数以简化人工免疫网络的参数设置。为了加快算法的搜索速度和搜索精度,提出了新型的人工免疫网络单纯形混合算法（PKAIN_spx）,用人工免疫网络实现粗粒度全局搜索,随后用单纯形进行精确搜索。仿真实验对改进的PKAIN算法（PKAIN_in）、人工免疫网络单纯形混合算法（PKAIN_spx）以及PKAIN算法进行了比较分析,结果表明PKAIN_spx算法在药代动力学参数优化中取得良好的实验效果。     

基于规范子块五点和的快速分形图像编码

分形图像编码通常需要较长的时间,编码时间主要花费于在一个海量码本中搜索每个输入子块的最佳匹配块.针对这个问题,提出一个限制搜索空间的算法.它主要基于图像块的一种新特征——五点和,把搜索范围限制在初始匹配块(五点和意义下与输入R块最接近的D块)的邻域内.实验表明:该算法能够大大减少子块匹配比较的数量,与基于叉迹的快速分形算法比较,在相同的搜索邻域内,在编码时间、图像质量和压缩比方面都更优.     

基于微粒群算法的纹理合成图像修补方法

提出了一种基于微粒群算法（PSO）的纹理合成图像修补方法。针对基于块的纹理合成算法中常用的匹配块全搜索效率低下的问题,对候选区域采样确定初始微粒,利用PSO算法搜索得到最优匹配块。实验结果表明了方法的有效性。     

一种改进的紧凑遗传算法及其在分形图像压缩中的应用

如何快速有效地为高频复杂区域找到合适匹配块是分形图像压缩中一个难以解决的问题。考虑到遗传算法的可并行性与全局搜索能力,结合匹配搜索的特点与要求,提出了一种逐位变异、最优保留的紧凑遗传算法,并将这种方法应用到高频复杂区域的匹配搜索中去。分析表明,这种算法具有较之其他随机搜索算法更好的收敛性、更高的搜索速度和全局搜索能力,能够大大提高匹配搜索中最优匹配块的捕获能力和搜索效率。实验结果也证明了这种算法在分形图像压缩匹配搜索算法中应用的优越性。     

一种改进的规范块半范数算法

提出一种改进的规范块半范数算法,对图像分割策略、码本块抽样收缩方式及搜索最近意义父块方式进行改进,以缩小子块搜索其最佳匹配父块的范围,加快编码速度,提高匹配准确度和解码图像质量。实验结果表明,与基于规范块半范数的快速算法相比,该算法的编码时间更短。     

改进转动惯量特征的快速分形图像编码算法

全搜索分形图像编码过程特别耗时的原因在于,每个range块都需要在一个很大的domain块池里寻找最佳匹配domain块。为了改进这个缺点,重新定义了图像规范块的转动惯量特征,证明了它与匹配均方根误差间的关系不等式,据此提出了一个限制搜索范围来加快编码过程的算法：一个待编码range块的最佳匹配块搜索范围仅在与它的转动惯量特征值相近的domain块的邻域内搜索,邻域半径的大小由预先设置的误差阈值来确定。三幅图像的仿真结果表明,它确实能够在不降低解码图像质量的情况下,通过减少搜索范围达到了平均加快全搜索分形编码算法的编码速度26倍左右（误差阈值为10）,且也优于转动惯量算法和三均值特征算法。     

视频图像运动估计中的一维块匹配算法

运动估计是视频图像压缩和视频图像修复等领域的基础问题,传统的块匹配法搜索质量较好,但搜索速度不够快.针对传统块匹配法搜索速度上的不足,提出一种快速的一维块匹配运动估计算法.首先对运动矢量正交分解,使用特殊权重系数矩阵对二维匹配块做降维处理,得到2组一维特征矩阵;然后选择一维三步搜索法作为搜索策略,最小绝对误差和准则作为匹配准则,使用2组一维特征矩阵搜索匹配运动矢量的2个分量;最后将分量组成完整的运动矢量.通过多组对比实验的结果表明,该算法在保证定量评价PSNR的前提下,显著提升运动估计的搜索速度,视频清晰度越高、匹配块像素尺寸越大,运动估计搜索速度提升越明显.     

一种基于块匹配的鲁棒运动估计算法

详细论述了基于块匹配的鲁棒运动估计算法。跟已有的基于块匹配的运动估计算法比较，首先，我们引入颜色信息来提高运动估计的准确性；其次，在更广泛的意义上运用自适应策略来减少计算量并同时保证算法的鲁棒性；最后，提出的基于预测修正的复合查找方法充分利用了物体运动的全局信息，克服了三步查找算法以及全查找算法的缺点并充分发挥它们二者的优点从而提高查找的效率和匹配精度。实验结果表明基于块匹配的鲁棒运动估计算法具有抗干扰能力强、运动估计准确、计算效率高等优点。     

A strategy of matching blocks at multi‐levels

We propose a size‐variable block‐matching algorithm for motion‐vector estimation. The proposed algorithm dynamically determines the search area and the size of a block. We exploit the constraint of small velocity changes of a block through time to determine the origin of the search area. The range of the search area is adjusted according to the motion coherency of spatially neighboring blocks. The process of determining the size of a block begins with the matching of a small block. If the matching degree is not good enough, we expand the size of the block slightly and then repeat the matching process until our matching criterion is satisfied. The experimental results show that the proposed algorithm can yield very accurate block motion vectors. © 2002 Wiley Periodicals, Inc.     

快速而有效的块运动估计算法

为了提高基于块匹配的运动估计的速度和精度,提出了一种带中心偏置点检测模式的自适应快速块运动估计算法。该算法根据图像序列的运动向量基于中心偏置分布的特点和相邻块运动向量间的高度相关性,依据块的不同运动内容来确定其搜索起点、搜索范围和搜索策略,从而实现块运动向量的快速而有效地估计。同时,对于大运动块,采用了多侯选者方式,进一步提高了搜索精度。实验结果表明,该算法的搜索速度接近N3SS,N4SS,而搜索精度比它们高,与HSS相似,接近FSBM。     

An efficient multi-layer reference frame motion estimation for video coding

Most of the fast search motion estimation algorithms reduce the computational complexity of motion estimation (ME) greatly by checking only a few search points inside the search area. In this paper, we propose a new algorithm—multi-layer motion estimation (MME) which reduces the computational complexity of each distortion measure instead of reducing the number of search points. The conventional fast search motion estimation algorithms perform ME on the reference frame with full distortion measure; on the contrary, the MME performs ME on the layers with partial distortion measures to enhance the computational speed of ME. A layer is an image which is derived from the reference frame; each macro-pixel value in the layer represents the sum of the values of the corresponding pixels in the reference frame. A hierarchical quad-tree structure is employed in this paper to construct multiple layers from the reference frame. Experimental results on different video sequences show evidence that many motion vectors have been found similar both in the reference frame and the layers. The effectiveness of the proposed MME algorithm is compared with that of some state-of-the-art fast block matching algorithms with respect to speed and motion prediction quality. Experimental results on a wide variety of video sequences show that the proposed algorithm outperforms the other popular conventional fast search motion estimation algorithms computationally while maintaining the motion prediction quality very close to the full-search algorithm. Moreover, the proposed algorithm can achieve a maximum of 97.99 % speed-improvement rate against the fast full-search motion estimation algorithms which are based on hierarchical block matching process. The proposed MME performs the motion estimation on the layers by using three types of search patterns. The derivation of these search patterns exploits the characteristic of the center-biased motion vector distribution and that of less intensive block distortion measurement of the layers.     

Dynamic initial search pattern defined on Cartesian product of neighboring motion vectors for fast block-based motion estimation

Block-based motion estimation is widely used in video compression for reducing the temporal data redundancy. However, it is still a main problem to effectively reduce the computational complexity of motion estimation. The median predictor is usually used for initial search center prediction, however it is not always accurate enough, especially for fast motion sequences. In this paper, a novel dynamic initial search pattern algorithm for fast block-based motion estimation is proposed. Based on the observation that the components of the current motion vector are very similar to the corresponding components of its neighboring motion vectors, Cartesian product of neighboring motion vectors is introduced to generate the proposed dynamic initial search pattern (DISP). And then the cross search pattern is employed to search for the best matching block. The number of search points of the proposed DISP is adaptive to the neighboring correlation of the current block. In fact, the proposed DISP can be considered as a generalization of median prediction scheme and it performs better in capturing the best matching block than median prediction. Experiment results show that the proposed DISP method with small cross search pattern can save about 1.71 search points on average compared with adaptive rood pattern search (ARPS) algorithm and can achieve the similar PSNR to full search (FS) algorithm by combining large cross search pattern.     

运动估计快速块匹配算法

基于块的运动估计是视频压缩国际标准中广泛采用的关键技术。在对目前运动估计快速块匹配算法研究的基础上，描述了运动估计的原理；揭示了在图像质量、搜索速度和压缩码率等方面提高算法效率时存在的3类主要问题：初始搜索点的选择、匹配准则和搜索策略；分别阐述了目前常用的解决这3类问题的方法，并进行了比较和分析；提出了对运动估计算法的一些展望。     

All-layer search algorithm using mean inequality and improved checkerboard partial distortion search for fast motion estimation

Block-matching motion estimation algorithm is used in many video compression coding systems because it could greatly reduce the temporal redundancy between the consequent video sequences. In this paper, an all-layer search algorithm using mean inequality and improved checkerboard partial distortion search scheme for fast block-matching motion estimation is proposed. A layer in the proposed method refers to a processed image which is derived from the reference frame or the adjacent lower layer. Firstly, the proposed algorithm constructs all layers from the reference frame or the adjacent lower layer by summing up all pixels over a sub-block. Then, a new mean inequality elimination method is introduced to reject a lot of unnecessary candidate search points on the top layers before calculating the real block matching distortion. Finally, the proposed algorithm utilizes an improved checkerboard partial distortion search scheme in the process of the real block distortion calculation on the following layers to further reduce the amount of computation. Experimental results show that the proposed algorithm can effectively reduce the computational complexity of motion estimation meanwhile guarantee the matching quality compared to other motion estimation algorithms. Compared to the full search algorithm, the proposed algorithm can reduce 97.30 % computational complexity with a negligible degradation of the peak signal to noise ratio (PSNR). Compared to the diamond search algorithm, directional gradient descent search algorithm, partial distortion search algorithm, transform-domain successive elimination algorithm and two-layer motion estimation algorithm, the proposed algorithm can also save 63.56 %, 52.73 %, 92.87 %, 85.77 % and 33.96 % computational complexity, respectively.     

A pattern based PSO approach for block matching in motion estimation

Block matching motion estimation is a popular method in developing video coding applications. A new algorithm has been proposed for reducing the number of search points using a pattern based particle swarm optimization (PSO) for motion estimation. The conventional particle swarm optimization has been modified to provide accurate solutions in motion estimation problems. This leads to very low computational cost and good estimation accuracy. Due to the center biased nature of the videos, the proposed approach uses an initial pattern to speed up the convergence of the algorithm. Simulation results show that improvements over other fast block matching motion estimation algorithms could be achieved with 31%～63% of search point reduction, without degradation of image quality.     

使用提前结束策略的部分失真搜索快速运动估计算法

和传统的快速块匹配运动估计算法相比，部分失真搜索运动估计算法通常会带来较低的视频图像质量下降，但它获得的计算加速比有限。本文提出一种新的快速部分失真搜索运动估计算法，它在搜索点检测过程中使用两种提前结束策略：早期搜索结束策略和中途搜索终止策略。提前结束策略的使用能够显著减少搜索点的个数，弥补了规格化部分
分失真搜索算法只能降低失真计算量的不足，两种策略的结合使用能够在保证较高视频质量的同时，大大降低运动估计的计算复杂性。实验结果及分析表明：新算法的性能优于传统的运动估计算法，在视频质量接近全局搜索算法的同时，获得比传统的运动估计算法更高的计算加速比。