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.     

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.     

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。     

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.     

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.     

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the 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 SAD values for all elements of the search window. Recently, 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 price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.     

Block-matching algorithm based on an adaptive reduction of the search area for motion estimation

The motion estimation and compensation technique is widely used for video coding applications but the real-time motion estimation is not easy due to its enormous computations. In this paper, a new adaptive reduction of search area for the block-matching algorithm is presented to reduce the computational complexity of the full search block-matching algorithm for low bit-rate video coding. The proposed method exploits the correlation of successive video frames and adjusts the size of search area depending on the displaced block difference and the block classification information in the previous frames of the block. Simulation results show that the proposed algorithm has similar mean square error performance to the full search block-matching algorithm but only requires less a half computational complexity than the full search algorithm.     

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.     

一种高效率的快速块匹配运动估计算法

采用菱形搜索算法对各种视频测试序列中运动矢量的研究,基于H.264视频编码标准提出了一种快速块匹配运动估计算法。它是以图像中相邻宏块之间的时空相关性为前提,结合了分布式菱形搜索,预测搜索和中止阈值等一系列技术而提出的,试验结果表明该算法在运算速度方面优于菱形搜索,而获得与全搜索相当的峰值信噪比。     

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。     

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.     

Improved FFSBM Algorithm and Its VLSI Architecture for AVS Video Standard

The Video part of AVS （Audio Video Coding Standard） has been finalized recently. It has adopted variable block size motion compensation to improve its coding efficiency. This has brought heavy computation burden when it is applied to compress the HDTV （high definition television） content. Based on the original FFSBM （fast full search blocking matching）, this paper proposes an improved FFSBM algorithm to adaptively reduce the complexity of motion estimation according to the actual motion intensity. The main idea of the proposed algorithm is to use the statistical distribution of MVD （motion vector difference）. A VLSI （very large scale integration） architecture is also proposed to implement the improved motion estimation algorithm. Experimental results show that this algorithm-hardware co-design gives better tradeoff of gate-count and throughput than the existing ones and is a proper solution for the variable block size motion estimation in AVS.     

H.264块运动估计自适应快速搜索算法研究

H.264视频编解码标准中由于运动搜索部分占整个编码时间的权重很大,所以运动搜索算法的优劣直接影响整个编码的效率。对编码运动估计理论进行了研究,采用六边形和小菱形相结合的方法,提出了一种新的自适应快速搜索算法来改善搜索效率。自适应快速搜索算法模式由当前块运动强度来决定,当当前块处于剧烈运动时,该算法选择六边形图形搜索算法;当当前块处于缓慢运动时,该算法选择小菱形图形搜索算法。实验结果表明该算法相比其他自适应算法以及单纯使用一种搜索图形节约了搜索时间,同时率失真性能影响甚微。     

H.264中运动搜索的自适应提前退出算法

在视频编码标准H.264中,运动搜索是占用编码时间最多的模块。为降低其运算量,基于4×4整数变换,在运动搜索算法中引入全零块判决机制,通过在整像素和1/2像素运动搜索过程中分别定义自适应的门限值来提前中止最匹配运动矢量的搜索。仿真结果表明,在编码性能损失很小的情况下,该算法可以大大降低运动搜索的复杂度。     

An efficient three-step search algorithm for block motion estimation

The three-step search algorithm has been widely used in block matching motion estimation due to its simplicity and effectiveness. The sparsely distributed checking points pattern in the first step is very suitable for searching large motion. However, for stationary or quasistationary blocks it will easily lead the search to be trapped into a local minimum. In this paper we propose a modification on the three-step search algorithm which employs a small diamond pattern in the first step, and the unrestricted search step is used to search the center area. Experimental results show that the new efficient three-step search performs better than new three-step search in terms of MSE and requires less computation by up to 15% on average.     

视频压缩感知中分级多假设预测算法

目的 多假设预测是视频压缩感知多假设预测残差重构算法的关键技术之一,现有的视频压缩感知多假设预测算法中预测分块固定,这种方法存在两点不足：1）对于视频帧中运动形式复杂的图像块预测效果不佳;2）对于运动平缓区域,相邻图像块的运动矢量非常相近,每块单独通过运动估计寻找最佳匹配块,导致算法复杂度较大。针对这些问题,提出了分级多假设预测思路（Hi-MH）,即对运动复杂程度不同的区域采取不同的块匹配预测方法。方法 对于平缓运动区域的图像块,利用邻域图像块的运动矢量预测当前块的运动矢量,从而降低运动估计的算法复杂度;对于运动较复杂的图像块,用更小的块寻找最佳匹配;对于运动特别复杂的图像块利用自回归模型对单个像素点进行预测,提高预测精度。结果 Hi-MH算法与现有的快速搜索预测算法相比,每帧预测时间至少缩短了1.4 s,与现有最优的视频压缩感知重构算法相比,对于运动较为复杂的视频序列,峰值信噪比（PSNR）提升幅度达到1 dB。结论 Hi-MH算法对于运动形式简单的视频序列或区域降低了计算复杂度,对于运动形式较为复杂的视频序列或区域提高了预测精度。     

基于全局运动补偿的多运动目标检测方法研究

提出了一种动态背景下的对多个运动目标检测的完整方法.利用基于宏块匹配的六参数全局运动估计方法进行全局运动补偿,有效地消除了摄像机在非稳定运动情况下对目标检测性能带来的不利影响.同时在宏块匹配前进行了预处理,通过预判提取纹理信息丰富的宏块,并在宏块匹配的过程中采用九点十字搜索算法取代传统的三步搜索算法,减少了匹配数据量....     

单纯形块匹配搜索算法

本文在讨论块运动估计快速算法的基础上 ,根据单纯形优化搜索原理 ,提出了一种新的块匹配搜索法——单纯形块匹配搜索算法 ,并经实验结果说明了该算法的有效性     

基于运动矢量投影的帧率转换算法

提出一种基于运动矢量投影的帧率转换算法。在运动估计阶段，采用连续消除算法SEA，将该算法与全搜索相结合，对块匹配准则的计算过程进行优化，可以在保证图像质量的同时减小计算复杂度。在运动矢量场投影过程中，定义一个新的运动矢量选取标准，在匹配准则的基础上添加了块的位置信息，相对于传统标准，本标准更能代表内插块的真实运动，准确性更高。在运动补偿阶段，针对投影过程中产生的重叠现象，采用自适应加权补偿插值算法，考虑所有重叠投影块的运动信息。对于产生的空洞现象，采用运动矢量中值滤波的算法来填充。实验结果表明，该算法可以减少运动信息的丢失，插值效果更加准确。     

一种适用于可视电话的快速运动估计算法

本文提出了一种适用于可视电话视频特性的快速运动估计算法。针对可视电话低码率的要求和头肩序列小运动的特性设计了一种计算量更小的块匹配方案。经过实验证明,这种算法可获得接近于全搜索算法的图像质量和信噪比。