A low-complexity line-based motion estimation algorithm and its hardware architecture

In this paper, a low-complexity motion estimation algorithm is presented. The proposed algorithm transforms a multiple-bit video sequence into a one-bit representation using a line-based approach. The Full Search Block-Matching Algorithm （FSBMA） is then carried out on the One-Bit- Transformed （1BT） video frames. The advantage of the proposed work is that it reduces the computational complexity of the motion estimation process without causing a significant decrease in the motion estimation performance. This paper also presents a hardware architecture to perform the line-based 1 BT in real-time.     

动态模式的快速运动估计算法

针对H.264视频编码标准中运动估计的高计算复杂度,提出了一种动态模式的快速运动估计算法。该算法通过判断宏块的运动大小及运动方向选择相应的搜索模式;同时对标准中的中值预测进行了改进并提出了一种动态的参考块提前跳过策略。实验结果表明,该算法在保持良好的率失真性能的基础上,减少了运动估计时间,相对于快速全搜索算法FFS以及UMHexagonS算法,该算法分别减少了85.28%和35.29%的运动估计时间。     

A real-time motion estimation FPGA architecture

A motion estimation architecture allowing the execution of a variety of block-matching search techniques is presented in this paper. The ability to choose the most efficient search technique with respect to speeding up the process and locating the best matching target block leads to the improvement of the quality of service and the performance of the video encoding. The proposed architecture is pipelined to efficiently support a large set of currently used block-matching algorithms including Diamond Search, 3-step, MVFAST and PMVFAST. The proposed design executes the algorithms by providing a set of instructions common for all the block-matching algorithms and a few instructions accommodating the specific actions of each technique. Moreover, the architecture supports the use of different search techniques at the block level. The results and performance measurements of the architecture have been validated on FPGA supporting maximum throughput of 30 frames/s with frame size 1,024 × 768.     

平行线段对应的运动估计线性算法

针对图像序列特征的运动估计问题,提出一种基于平行线段对应的运动估计线性算法(parallel linesegments,PLS).线段采用点、线两要素表述模型,利用平行性,由像线段逐步恢复场景中的空间线段,再根据螺旋理论四元数法建立并求解基于空间线段两要素的线性运动约束方程,进一步建立粒子群优化算法(PSO)优化算法优...     

Real time motion estimation using a neural architecture implemented on GPUs

This work describes a neural network based architecture that represents and estimates object motion in videos. This architecture addresses multiple computer vision tasks such as image segmentation, object representation or characterization, motion analysis and tracking. The use of a neural network architecture allows for the simultaneous estimation of global and local motion and the representation of deformable objects. This architecture also avoids the problem of finding corresponding features while tracking moving objects. Due to the parallel nature of neural networks, the architecture has been implemented on GPUs that allows the system to meet a set of requirements such as: time constraints management, robustness, high processing speed and re-configurability. Experiments are presented that demonstrate the validity of our architecture to solve problems of mobile agents tracking and motion analysis.     

A fast VLSI architecture of a hierarchical block matching algorithm for motion estimation

In this paper, an efficient VLSI architecture of a hierarchical block matching algorithm has been proposed for motion estimation. At the lowest resolution level, two motion vector (MV) candidates are selected to get better performance. In the next search level, these two candidates provide the center points for local searches to get one MV candidate. Then, at next level and the finest level, one MV candidate is chosen from one local search area (LSA), defined by the MV candidate, obtained from lower resolution level. This architecture requires nine processing elements and data are processed in such a way that calculation to obtain frames of different resolution is overlapped with the MV calculation. Simulation results indicate that this architecture is more area-efficient and faster than many full-search, three-step-search and multiresolution architectures which makes it suitable for SD and HD video. To avoid the delay due to pipelining, the MVs of all the macro-blocks are calculated for one resolution level and stored in RAM to get LSA for next resolution level. This architecture with about 16 K gates is implemented for a search range of [?15, +15]. As this architecture requires only two-port memory, which is very common in most consumer electronics systems, it can be integrated easily in any existing system at the expense of a very small area.     

利用全局运动估计和Canny理论压缩显微图像

随着全自动生物显微镜的应用越来越广泛,高分辨率显微图像数据量大的问题越来越突出。为了使其便于存储及网络传输,针对全自动生物显微镜下图像步进式移动的特点,提出了基于全局运动估计和Canny边缘检测算法的显微图像压缩算法,利用Canny边缘检测算法、全局运动矢量估计及运动补偿等技术对图像进行压缩。实验结果表明,该算法对高分辨率生物显微图像的压缩比可以达到600倍以上。     

Fast and accurate global motion estimation algorithm using pixel subsampling

Global motion generally describes the motion of a camera, although it may comprise motions of large objects. Global motions are often modeled by parametric transformations of two-dimensional images. The process of estimating the motions parameters is called global motion estimation (GME). GME is widely employed in many applications such as video coding, image stabilization and super-resolution. To estimate global motion parameters, the Levenburg–Marquardt algorithm (LMA) is typically used to minimize an objective function iteratively. Since the region of support for the global motion representation consists of the entire image frame, the minimization process tends to be very expensive computationally by involving all the pixels within an image frame. In order to significantly reduce the computational complexity of the LMA, we proposed to select only a small subset of the pixels for estimating the motion parameters, based on several subsampling patterns and their combinations. Simulation results demonstrated that the proposed method could speed up the conventional GME approach by over ten times, with only a very slight loss (less than 0.1 dB) in estimation accuracy. The proposed method was also found to outperform several state-of-the-art fast GME methods in terms of the speed/accuracy tradeoffs.     

一种改进的运动估计新算法

提出一种改进的新三步搜索法（NITSS）。该方法充分利用视频序列运动矢量概率分布上的中心偏置特性,在三步搜索算法的基础上引入了六边型分布的6个点构成搜索点群,解决了三步法的小运动估计效果较差问题。实验结果表明,同TSS算法相比,NITSS算法降低了搜索运算量,提高了搜索精度。     

快速自适应运动估计算法

为降低H.264中运动估计的复杂性,通过对自然图像序列的分析,提出了一种基于块的运动特征的快速自适应搜索算法。该算法充分利用视频序列中当前块的运动状态,根据不同块的运动情况合理选择运动搜索模板。实验表明,该算法在保证重构图像质量的前提下,编码速度有了显著提高,保证了实时应用的要求。     

基于运动特征的自适应运动估计算法

提出了一种新的基于运动特征的自适应运动估计算法。该算法主要基于两方面：（1）建立具有自适应特性的搜索起点预测模型,根据运动相关性的变化调整模型参数,使预测结果更加接近最佳运动矢量。（2）采用的搜索模板可以根据物体的运动特征调整大小和形状,从而提高搜索效率。实验结果表明,该算法在PSNR和搜索速度两方面均明显优于常用的快速算法。     

Scalable high-throughput variable block size motion estimation architecture

Variable block size (VBS) motion compensated prediction (MCP) provides substantial rate-distortion performance gain over conventional fixed-block-size MCP and is a key feature of the H.264/AVC video coding standard. VBS–MCP requires the encoder to perform VBS motion estimation (VBSME), a computationally complex operation. In this paper, we propose a high motion vector throughput full-search VBSME architecture. High performance is achieved by performing parallel computations for multiple pixels within a macroblock, as well as computing several candidate motion vector (MV) positions in parallel. Two implementations of the architecture are examined, a four pixel-parallel implementation, and a higher performance 16 pixel-parallel implementation. A high degree of scalability is achieved by allowing for a variable length processing element array, where more processing elements yields a higher degree of candidate MV parallelism. The proposed architecture achieves a throughput exceeding current full-search VBSME architectures.     

一种有效的自适应运动估计搜索算法

提出了一种新的基于运动矢量场、方向自适应和半像素搜索的快速搜索算法(M-DAHS)。该算法根据图像序列运动矢量场的中心偏置性和时空相关性进行预判,对静止块设定阈值直接终止搜索;非静止块根据运动类型自适应选择搜索起始点和搜索策略。搜索模板具有很强的方向自适应性,对于小运动块采用菱形-线性搜索,其他块使用六边形-菱形搜索算法。整像素搜索完毕后,再以十字优先原则进行半像素搜索。实验结果表明,该算法性能优越,搜索速度快,搜索精度高,且搜索精度可以非常接近全搜索算法。     

一种运动估计的自适应菱形算法

在菱形搜索算法的基础上,依据图像序列的运动矢量的时空相关性和中心偏移特性,首先对宏块进行类型划分、设定阀值,进一步提出了初始搜索点的预测。实验证明,该算法在保证图像质量的同时,大大提高了搜索速度。     

利用运动强度判据的高效自适应运动估计算法

为减少运动估计计算量,提高视频编码效率,提出了基于运动强度的自适应运动估计搜索算法。该算法通过定义运动强度概念来反映帧间图像运动的剧烈程度,依据当前帧的运动强度信息预测下一帧运动情况,并自适应选择算法进行运动搜索:当运动强度高于设定阈值时选用UMHexagonS算法,低于该阈值时选用改进的六边形算法。实验仿真结果表明,该算法能在保证图像质量和压缩效果的基础上,大幅提高编码效率,并可通过调节阈值大小满足不同编码要求。     

基于运动信息自适应的快速运动估计算法

UMHexagonS是H.264视频编码标准中所采用的快速整像素运动估计算法,但在许多实时场景的应用中,该算法还明显存在搜索点数过多、搜索速度较慢的缺憾,急需进一步的改进和优化。在UMHexagonS算法的基础上,提出一种基于运动信息自适应的快速运动估计算法。使用动态搜索窗为不同尺寸的块自适应地分配预测搜索窗;根据当前块的运动剧烈程度选择运动类型自适应的搜索方案;通过分析实际运动序列水平、垂直方向的偏向特性依次采用带方向的十字型搜索和自适应的矩形—菱形搜索;利用预测运动矢量的方向信息采用自适应的多层次八边形区域搜索;并依据块的尺寸大小采用自适应的六边形搜索。实验结果表明,本文算法相比于UMHexagonS算法而言,图像的峰值信噪比(PSNR)平均提高了0.0125 dB,同时运动估计时间减少了13%32%,其场景自适应能力和实时性能都得到了很大的增强。     

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.     

Optic flow field segmentation and motion estimation using a robustgenetic partitioning algorithm

Optic flow motion analysis represents an important family of visual information processing techniques in computer vision. Segmenting an optic flow field into coherent motion groups and estimating each underlying motion is a very challenging task when the optic flow field is projected from a scene of several independently moving objects. The problem is further complicated if the optic flow data are noisy and partially incorrect. In this paper, the authors present a novel framework for determining such optic flow fields by combining the conventional robust estimation with a modified genetic algorithm. The baseline model used in the development is a linear optic flow motion algorithm due to its computational simplicity. The statistical properties of the generalized linear regression (GLR) model are thoroughly explored and the sensitivity of the motion estimates toward data noise is quantitatively established. Conventional robust estimators are then incorporated into the linear regression model to suppress a small percentage of gross data errors or outliers. However, segmenting an optic flow field consisting of a large portion of incorrect data or multiple motion groups requires a very high robustness that is unattainable by the conventional robust estimators. To solve this problem, the authors propose a genetic partitioning algorithm that elegantly combines the robust estimation with the genetic algorithm by a bridging genetic operator called self-adaptation