一种基于时空相关性的运动估计算法

研究了一种基于时空相关性的运动估计方法。提出在块匹配法中,对于运动相关性好的块根据相邻块或前一帧图像的运动预测出它的初始运动矢量,然后在小范围内搜索,做运动矢量的优化。对传统的块匹配运动估计,给出了提高运动矢量一致性的修正准则。实验结果表明,本文算法所得的运动矢量一致性远高于传统块匹配法,同时能保持和全局搜索法相媲美的预测质量,而运算时间则有较大幅度缩短。     

一种基于时空相关性的运动估计算法

研究了一种基于时空相关性的运动估计方法。提出在块匹配法中,对于运动相关性好的块根据相邻块或前一帧图像的运动预测出它的初始运动矢量,然后在小范围内搜索,做运动矢量的变化。对传统的块匹配运动估计,给出了提高运动矢量一致性的修正准则。实验结果表明,本文算法所得的运动矢量一致性远高于传统块匹配法,同时能保持和全局搜索法相媲美的预测质量,而运算时间则有较大幅度缩短。     

一种新的自适应二值运动估计算法

利用增强位平面的二值特性和运动矢量场自适应思想,提出了一种新的自适应二值运动估计算法.算法在图像的位平面中完成运动矢量的搜索;匹配时对静止块进行判定直接中止搜索,同时采用了起始点预测和运动类型判定,自适应使用LDSP和SDSP模板进行搜索.实验结果表明:该算法的搜索速度优于绝大多数现有的运动估计算法,且预测精度接近于FS算法.     

一种预测质量可控的快速运动估计搜索算法

本文提出了一种预测质量可控的快速运动估计搜索算法.该算法利用运动矢量的空间、时间相关性,通过预测初始搜索中心位置、判定是否为静止宏块、调整搜索窗口、预测质量可控的渐进式块匹配准则搜索等步骤进行运动估计,并引入调节函数在预测质量和搜索速度之间增加了可控性.实验结果表明本算法大大减少了计算量,明显提高了运算速度,且具有很好的重建视频质量,适合于在低码率视频编码中应用.     

一种用于视频编码的块运动估计算法——块特征匹配预测搜索算法

本文提出了一种块特征匹配预测搜索ＢＦＭＰＳ算法,可以用于视频压缩的一些国际标准,如Ｈ．２６１,Ｈ．２６３,ＭＰＥＧ１,ＭＰＥＧ２,ＨＤＴＶ中,ＢＦＭＰＳ算法充分利用了序列图像的实际矢量与预测矢量之间距离的空间分布特征,中心偏置分布特性和时间上的相关特性,并采用了中止判决准则,可以明显地减少了运动搜索复杂度,ＢＦＭＰＳ算法在匹配运算中采用 简单有交的块特征匹配准则函数,相应的块匹配计算复杂度,数据读     

基于H.264自适应阈值的快速运动估计算法

块匹配运动估计在视频编码中有着举足轻重的地位,全搜索算法是最好的搜索算法,但其计算量是最大的,所以提出一种H.264自适应阈值的快速运动估计算法.该算法是利用相邻块的运动矢量来预测初始搜索点,并根据一定的准则来确定静止块,减少了搜索的冗余度,再用基于梯度的十字优先菱形算法米进行搜索.与DS菱形算法相比,该算法具有更高的灵活性,能实时的提前退出搜索,并且在搜索点数上明显减少,且没减少搜索精度.     

基于 SIMD-MCC 的图象块匹配并行算法

图像块匹配操作是图像处理中很多基于窗口任务的典型操作之一。文章提出了一种在SIMD-MCC计算机上实现的全搜索图像块匹配的并行算法,此算法对实时图中的每一个参考块和参考图中搜索区中的候选块进行比较,以确定一个最小的位移矢量。这个位移矢量所对应的位置就是匹配位置。该算法计算复杂度为O(log2N)。     

创新的高效方向性菱形-弧形快速块匹配运动估计算法

针对传统运动估计算法的缺点,本文提出了一种采用弧形、小十字、大菱形模板进行快速块匹配运动估计的新算法.该算法(NOADS)充分利用序列图像中运动矢量场中心偏置分布特性,使用小十字模板,进一步搜索根据情况自适应调整扩展为水平菱形或是垂直菱形模板,处理中心区域小运动矢量和静止运动矢量的搜索.使用大菱形-弧形模板处理大运动矢量的搜索.实验结果表明NOADS有效减少了搜索点数,提高了搜索速度,能同时适应于小运动块和大运动块的搜索,速度上比DS提高约20%,比3SS提高30% ～60%.     

结合十字搜索的三角形搜索算法

提出一种新的运动估计快速块匹配算法,并结合十字搜索的三角形搜索算法(PCTS),利用序列图像运动矢量场所具有的中心偏置性和空间相关性先进行起始点预测的十字搜索,并做出提前终止判断,再利用单纯形最优化理论做三角形搜索.实验结果表明,该算法在确保搜索准确度的同时大大减少了搜索点的个数.     

一种低功率分层运动估值器的VLSI结构设计

本文提出一种新的低功率分层运动估值器的VLSI结构,它支持低比特视频编码器的高级预测模式,如H.263和MPEG-4。为减少芯片尺寸及功率消耗,在所有搜索层中使用同一个基本的搜索单元 (BSU)。另外,通过对数据流的有效控制,使其在高级预测模式下,在获得宏块运动矢量的同时,也获得每个宏块中的4个88子块的运动矢量。实验结果表明,这种结构采用较少的门电路,有效降低了功率消耗,并且实现了与全搜索块匹配算法(FSBMA)相似的编码效果,可广泛应用于无线视频通信所需的低功率视频编码器中。     

MOTION COMPENSATION BASED ON MOTION VECTOR INTERPOLATION

A modified block matching algorithm (BMA) with motion correlation constraint (MCCBMA) is proposed at first. Then a novel motion compensation algorithm (INTPMC) which computes motion vector for each pixel by interpolating motion vectors is presented. In order to increase interframe prediction performance and decrease the computational complexity, an optimizing algorithm for partial motion vectors is described at last. Experimental results show that the proposed algorithm can improve the prediction performance obviously with a moderately increased complexity compared with the conventional full search block matching algorithm (FSBMA).     

A fast motion estimation algorithm based on the block sum pyramid

In this correspondence, a fast approach to motion estimation is presented. The algorithm uses the block sum pyramid to eliminate unnecessary search positions. It first constructs the sum pyramid structure of a block. Successive elimination is then performed hierarchically from the top level to the bottom level of the pyramid. Many search positions can be skipped from being considered as the best motion vector and, thus, the search complexity can be reduced. The algorithm can achieve the same estimation accuracy as the full search block matching algorithm with much less computation time.     

Frequency-domain motion estimation using a complex lapped transform

A frequency-domain algorithm for motion estimation based on overlapped transforms of the image data is developed as an alternative to block matching methods. The complex lapped transform (CLT) is first defined by extending the lapped orthogonal transform (LOT) to have complex basis functions. The CLT basis functions decay smoothly to zero at their end points, and overlap by 2:1 when a data sequence is transformed. A method for estimating cross-correlation functions in the CLT domain is developed. This forms the basis of a motion estimation algorithm that calculates vectors for overlapping, windowed regions of data. The overlapping data window used has no block edge discontinuities and results in smoother motion fields. Furthermore, when motion compensation is performed using similar overlapping regions, the algorithm gives comparable or smaller prediction errors than standard models using exhaustive search block matching, and computational load is lower for larger displacement ranges and block sizes.     

Efficient tree structured motion estimation using successive elimination

In H.264/AVC, tree structured motion estimation enhances the coding efficiency significantly while dramatically increasing the computational complexity of block matching. In the paper, a successive elimination algorithm (SEA) is implemented in tree structured motion estimation with a simple and effective method to determine the initial motion vector, which exploits the strong correlation among the partially overlapped variable-size blocks. With identical performance to a full search algorithm, computations for block matching can be reduced to 1%-20%. Further, the SEA can be improved by incorporating two early termination conditions, then named 'Quick SEA'. Finally, a novel fast motion estimation algorithm, successive elimination diamond search (SEDS), is proposed by efficiently integrating the Quick SEA and a modified diamond search pattern. Simulation results show that the proposed Quick SEA can reduce the computational complexity of block matching by 3-5 times compared to the basic SEA. SEDS further reduces by about one-half the computations of Quick SEA. With similar rate distortion performance, 0.2%-1% block matching distortion is calculated for SEDS with corresponding speed-up factors of 100 to 500 in comparison with the full search algorithm.     

一种运动估计的快速预测搜索算法

本文提出了一种运动估计的快速预测搜索算法(PSA)。该算法首先用当前块的三个邻近块运动矢量的线性加权来得到预测矢量,然后以预测点为起始点,采用33的搜索窗进行搜索步长为1的移动窗搜索,直到搜索到达搜索域的边界或搜索的局部最小点位于搜索窗的中心时停止。该算法由于利用了序列图象的实际运动矢量与预测矢量之间距离的空间分布特性一中心偏置分布特性和时间上的相关特性,并采用了中止判决准则,可以明显地减少搜索次数。仿真表明这种算法减少了搜索范围和搜索次数,提高了搜索效率,降低了运动估计的计算复杂性。本文还详细地给出了PSA算法与其它常用快速算法的比较结果。     

Joint estimation of forward and backward motion vectors forinterpolative prediction of video

A low-complexity iterative algorithm is introduced for joint estimation of forward and backward motion vectors in interpolative prediction of video. Starting from the initial values obtained by a commonly-used block matching independent search method, the motion vectors are iteratively refined until a locally optimal solution to the motion estimation problem for interpolative prediction is achieved. Each iteration consists of a series of two similar procedures. First, the backward motion vector is fixed and a new forward motion vector is searched to minimize the interpolation error. Then the forward motion vector is fixed and the backward motion vector is similarly refined by minimizing the interpolation error. This process is repeated until the interpolation error stops decreasing. Computer simulation results demonstrate that with this technique the prediction error in some scenes is significantly reduced.     

Novel two-layer motion estimation for video coding

In this paper,we proposed a novel Two-layer Motion Estimation （TME） which searches motion vectors on two layers with partial distortion measures in order to reduce the overwhelming computational complexity of Motion Estimation （ME） in video coding.A layer is an image which is derived from the reference frame such that the sum of a block of pixels in the reference frame determines the point of a layer.It has been noticed on different video sequences that many motion vectors on the layers are the same as those searched on the reference frame.The proposed TME performs a coarse search on the first layer to identify the small region in which the best candidate block is likely to be positioned and then perform local refined search on the next layer to pick the best candidate block in the located small area.The key feature of TME is its flexibility of mixing with any fast search algorithm.Experimental results on a wide variety of video sequences show that the proposed algorithm has achieved both fast speed and good motion prediction quality when compared to well known as well as the state-of-the-art fast block matching algorithms.     

Predictive RD optimized motion estimation for very low bit-ratevideo coding

Predictive rate-distortion (RD) optimized motion estimation techniques are studied and developed for very low bit-rate video coding. Four types of predictors are studied: mean, weighted mean, median, and statistical mean. The weighted mean is obtained using conventional linear prediction techniques. The statistical mean is obtained using a finite-state machine modeling method based on dynamic vector quantization. By employing prediction, the motion vector search can then be constrained to a small area. The effective search area is reduced further by varying its size based on the local statistics of the motion field, through using a Lagrangian as the search matching measure and imposing probabilistic models during the search process. The proposed motion estimation techniques are analyzed within a simple DCT-based video coding framework, where an RD criterion is used for alternating among three coding modes for each 8×8 block: motion only, motion-compensated prediction and DCT, and intra-DCT. Experimental results indicate that our techniques yield very good computation-performance tradeoffs. When such techniques are applied to an RD optimized H.263 framework at very low bit rates, the resulting H.263 compliant video coder is shown to outperform the H.263 TMN5 coder in terms of compression performance and computations simultaneously     

Efficient multilevel successive elimination algorithms for block matching motion estimation

The authors present fast algorithms to reduce the computations of block matching algorithms, for motion estimation in video coding. Efficient multilevel successive elimination algorithms are based on the multilevel successive elimination. Efficient multilevel successive elimination algorithms consist of four algorithms. The first algorithm is given by the sum of absolute difference between the sum norms of sub-blocks in a multilevel successive elimination algorithm (MSEA) using the partial distortion elimination technique. By using the first algorithm, computations of MSEA can be reduced further. In the second algorithm, the sum of absolute difference (SAD) is calculated adaptively from large value to small value according to the absolute difference values between pixels of blocks. By using the second algorithm, the partial distortion elimination in the SAD calculation can occur early, therefore the computations of MSEA can be reduced. The second algorithm is useful not only with MSEA, but also with all kinds of block matching algorithms. In the third algorithm, the elimination level of the MSEA can be estimated. Accordingly, the computations of the MSEA related to the level lower than the estimated level can be reduced. The fourth algorithm is, first of all, to search the motion vector over the half sampled search points. At the second search, the authors search the unsampled search points around the tested search points where the motion vector may exist from the first search results. The motion estimation accuracy of the fourth algorithm is nearly 100% and the computations can be greatly reduced.