Block-matching algorithm based on differential evolution for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame (macro block, MB) can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing the sum of absolute differences (SAD) produced by the MB of the current frame over a determined search window from the previous frame. The SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. The most straightforward BM method is the full search algorithm (FSA), which finds the most accurate motion vector, exhaustively calculating the SAD values for all the elements of the search window. Over this decade, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the cost of poor accuracy. In this paper, a new algorithm based on differential evolution (DE) is proposed to reduce the number of search locations in the BM process. To avoid computing several search locations, the algorithm estimates the SAD values (fitness) for some locations using the SAD values of previously calculated neighboring positions. As the proposed algorithm does not consider any fixed search pattern or any other different assumption, a high probability for finding the true minimum (accurate motion vector) is expected. In comparison with other fast BM algorithms, the proposed method deploys more accurate motion vectors, yet delivering competitive time rates.     

Block-matching algorithm based on harmony search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to save computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The proposed method has been compared to other BM algorithms in terms of velocity and coding quality. Experimental results demonstrate that the proposed algorithm exhibits the best balance between coding efficiency and computational complexity.     

一种基于块分割结构的运动估计新算法

运动估计是剔除视频压缩中的时间冗余的关键,现有算法大都是基于全搜索策略的SAD匹配算法,这些算法虽然压缩性能很好,但计算复杂,实时性差。提出一种快速运动估计新算法,将块分割成多个子块,计算每个子块的灰度值之和与灰度值的平方和,将其整体作为一个参数再结合提出的三个匹配准则,求出当前帧和候选帧之间的最优运动估计。通过实验表明,采用该算法后计算的复杂度明显减小,实时性得到较大提高,其压缩性能却非常接近基于全搜索策略的SAD算法。     

一种快速半像素运动矢量搜索算法

由于快速整像素搜索算法的提出和采纳,致使半像素搜索过程在整个编码过程中占用的比重更为显著。为了减小半像素搜索的计算量,提出了一种快速半像素运动矢量搜索算法。该算法是利用半像素搜索窗内的误差匹配曲面具有的单峰特性,通过比较整像素运动矢量周围4个整像素点的绝对误差和(SAD)来实现可能的最小匹配误差半像素点的预测,以排除大量不必要的计算量。实验结果表明,该算法对于各种不同运动程度和空间细节的视频序列,在保证和半像素全搜索法有相同图像质量的同时,至少可节省66%的计算量。     

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.     

基于起点预测和SAD分布的快速运动估计算法

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。文中提出了结合相邻块运动向量相等和SAD值比较的起点预测方法,减少了起点预测时计算SAD的开销;利用SAD分布的方向性,对SAD值偏小部分重点搜索,加速了块匹配的快速搜索策略。在此基础上设计了一种新的快速运动估计算法,该算法在大幅度提高搜索效率的同时,得到了与全搜索非常接近的搜索结果,从而减少或避免了不必要的搜索。     

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.     

H．264中带有中途停止的多层逐次消元运动估计算法

H．264视频压缩标准采用多模式运动估计，可以有效减少块匹配预测误差，但随着模式选择的增多，算法计算量成倍增加。为此，提出一种带有中途停止的多层逐次消元运动估计算法(MSEHS)。该算法根据模式分布规律，使用多层逐次消元法加速大块模式的搜索过程，并且提出中途停止准则，判断是否继续进行小块模式的搜索。这样既保证了多模式运动估计的优点，又减少了冗余计算。实验结果表明，该算法比全搜索算法整体速度提高了近4倍，同时可以保持与全搜索算法非常接近的图像质量和比特率。     

基于最小匹配误差方向预测的快速半像素运动估计

为了减小半像素搜索的计算量,本文提出了一个基于最小匹配误差方向预测的快速半像素运动估计算法.本文提出的算法利用亚像素搜索窗内的匹配误差单峰曲面的特性来预测半像素搜索区域中最小匹配误差方向,从而避免了大量不必要的匹配运算量.实验结果表明,对于各种不同运动程度和空间细节的视频序列,本文提出的算法在保证和半像素全搜索法相同图像质量的同时,平均节省73%的计算量,很适合实时应用.     

Improved Normalized Partial Distortion Search With Dual-Halfway-Stop for Rapid Block Motion Estimation

Motion estimation is a critical yet computationally intensive task for video encoding. In this paper, we present an enhancement over a normalized partial distortion search (NPDS) algorithm to further reduce block matching motion estimation complexity while retaining video fidelity. The novelty of our algorithm is that, in addition to the halfway-stop technique in NPDS, a dual-halfway-stop (DHS) method, which is based on a dynamic threshold, is proposed, so that block matching is not performed against all matching candidates. An adaptive search range (ASR) mechanism based on inter block distortion further constrains the searching process. Simulation results show that the proposed algorithm has a remarkable computational speedup when compared to that of full search and NPDS algorithms. Particularly, it requires less computation by 92-99% and encounters an average of only 0.08 dB PSNR video degradation when compared to that of full search. The speedup is also very significant when compared to that of fast motion estimation algorithms. This paper describes our work that led to our joint video team (JVT) adopted contribution (included in software JM 10.1 onwards) as well as later enhancements, collectively known as simplified and unified multi-hexagon search (SUMH), a simplified fast motion estimation.     

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.     

基于多级顺序排除算法(MSEA)的改进

块匹配运动估计是视频编码国际标准中广泛采用的关键技术.许多快速块匹配法通过限制搜索点数来减少运算量,但与全搜索算法相比极易出现匹配误差.该文介绍了一种应用新的判别条件的多级顺序排除算法（MSEA）,并在此基础上提出一种新的算法,该算法引入了尺度化的部分失真消除（PDE）技术,用于尺度化累积部分误差和当前最小误差.实验证明,相对于一脉相承的同为穷举搜索算法的全搜索算法（FS）、顺序排除算法（SEA）、多级顺序排除算法（MSEA）等,该算法大幅度提高了搜索效率.与多级顺序排除算法相比,平均每宏块节省了大约75%的运算次数.该算法在保证图像质量的前提下,使视频编码的速度大大提高.     

Simple and efficient motion estimation algorithm by continuum search

Motion estimation plays a vital role in reducing temporal correlation in video codecs but it requires high computational complexity. Different algorithms have tried to reduce this complexity. However these reduced-complexity routines are not as regular as the full search algorithm (FSA). Also, regularity of an algorithm is very important in order to have a hardware implementation of that algorithm even if it leads to more complexity burden. The goal of this paper is to develop an efficient and regular algorithm which mimics FSA by searching a small area exhaustively. Our proposed algorithm is designed based on two observations. The first observation is that the motion vector of a block falls within a specific rectangular area designated by the prediction vectors. The second observation is that in most cases, this rectangular area is smaller than one fourth of the FSA’s search area. Therefore, the search area of the proposed method is adaptively found for each block of a frame. To find the search area, the temporal and spatial correlations among motion vectors of blocks are exploited. Based on these correlations, a rectangular search area is determined and the best matching block in this area is selected. The proposed algorithm is similar to FSA in terms of regularity but requires less computational complexity due to its smaller search area. Also, the suggested algorithm is as simple as FSA in terms of implementation and is comparable with many of the existing fast search algorithms. Simulation results show the claimed performance and efficiency of the algorithm.     

运动矢量加速度预测起始搜索点的块匹配算法

块匹配运动估计是去除图像序列时间冗余的重要手段,在MPEG-4、H.264/AVC等视频编码标准中都得到了应用,但消耗了巨大的运算量.论文阐述了块匹配算法原理,归纳了当前运动估计中采用的各种手段,建议使用一种运动矢量加速度预测搜索起点的算法,利用相邻的若干参考帧中对应块的运动加速度来预测待编码块的起始运动矢量.仿真结果证明该方法效果明显,减少了搜索次数且准确度高.     

自适应选择编码模式的快速运动估计算法

针对目前运动估计算法中分割块选择与阈值造成的算法搜索冗余现象,提出一种自适应选择编码模式的快速运动估计算法。在阈值的选取上通过提出自适应的阈值选取方式,来提高算法的搜索精度。而在模块的选取上,则是通过结合视频图像的空间相关性与运动特性来对其进行择优,并采用几种搜索模板来对分割块进行搜索,进而降低运动估计模块的计算复杂度。实验结果表明,该算法能够在得到与全搜索算法相近重构图像质量的情况下减少97%的运算时间。     

一个健壮的用于低比特率视频编码的快速运动估计算法

提出一个用于低比特率视频编码的快速块匹配运动估计算法，该算法能够自适应调整搜索起点使其接近全局最优位置，并采用有效的搜索模式来防止搜索过程陷入局部最小，同时用灵活的中止准则来控制运动估计精度与计算量，实验结果表明，该算法非常健壮，所需的计算量仅为全搜索算法的1/50，但能够取得与其相似的运动估计性能。本算法在实时视频编码中具有重要的应用价值。     

基于时空域相关预测的步长自适应运动估计算法

为了更好地消除视频中空间和时间冗余,快速并有效地获得足够精度的运动矢量,本文提出一种改进的自适应十字搜索算法。本文算法利用时间空间域相关来预测当前块的运动矢量,对于视频的边缘图像采取固定小步长来进行十字搜索,对于图像的非边缘部分则采取由粗到精的方式进行搜索,搜索模板的自适应臂长为预测得到的目标运动矢量的横纵坐标的最大值。通过实验仿真比较传统的自适应十字搜索算法及其他几种经典的运动估计算法,结果表明本文算法增强了搜索预测的准确性,减少了平均每块搜索的次数,提高了搜索速率。     

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.     

Fast motion estimation based on search range adjustment and matching point decimation

This study introduces a new fast motion estimation (ME) based on both an adaptive search range adjustment and a matching point decimation. In particular, the authors present a maximum matching error constraint in the matching phase that can eliminate an impossible candidate block much earlier than a conventional partial distortion elimination (PDE) scheme. The constraint is computed during the matching error computation based on sum of absolute difference (SAD) between two blocks. The basic idea of the proposed scheme is based on adjusting a given search range adaptively and early eliminating invalid matching blocks effectively. The adaptive search range adjustment is first performed by analysing the contents of a scene. Next, a maximum partial matching error in reordered sub-blocks of an optimal block is obtained, and it is set as a trigger to eliminate invalid blocks for ME. The main contributions of the proposed scheme are that (i) it can reduce a search range adaptively based on the analysis of scene contents; (ii) it can make an early decision for an impossible candidate before complete SAD computation; (iii) the proposed constraint can reduce the computational cost considerably for SAD calculation; and (iv) the proposed matching ideas can be applied to conventional PDE algorithms without significant changes. In order to evaluate the proposed scheme, several baseline approaches are described and compared. The experimental results show that the proposed algorithm can reduce the computational cost more than 86% for ME at the cost of 0.02%dB quality degradation on against the conventional PDE algorithm.     

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.