基于节点交叉搜索的可变形块匹配运动估计算法

文章在讨论可变形块(DBMA)的运动估计和基于节点位移的可变形块运动模型的基础上,提出了基于节点交叉搜索的可变形块匹配算法(CS-DBMA).以四节点模型为例,详细研究了该算法的搜索过程和流程,并利用两个序列进行测试,测试结果表明,在预测效果减少很小的情况下,CS-DBMA算法的运算量大大的减少.     

基于节点搜索的可变形块运动补偿

本文讨论可变形块匹配(DBMA)的运动补偿和预测方法,提出基于节点搜索的可变形块匹配算法(NS-DBMA),并在此基础上提出分数像素精度预测和双模式混合预测方法.实验结果表明,NS-DBMA比全搜索方块匹配法(EBMA)平均改善约2dB;其运算量仅为基于梯度的可变形块匹配算法(GB-DBMA)的一半,但能得到更好的主客观预测质量,且更易于VLSI硬件实现.     

一种基于H．264的有效运动估计算法

运动估计算法是实时视频编解码技术的研究重点，高精度的匹配和补偿可以减少预测误差，提高视频图像的压缩效果．为降低在视频编码标准H．264中运动估计的高计算复杂度问题，提出了采用一种基于节点模型的可变形块匹配运动估计算法来搜索最佳运动矢量．该算法充分利用了H．264运动矢量的的统计特性和相关性，并采用基于像素差值分类的运动估计匹配准则．实验表明，在编码性能损失很小的条件下，该算法有效降低了视频压缩编码中运动估计的运算复杂度．     

基于网格模型的运动估计技术

网格模型的运动估计和跟踪在基于模型的编码方法中占有重要的地位,在现有的网格运动估计技术中,六边形匹配(Hexagonal Matching)是一种有效的网格模型运动估计算法,它采用局部最优和迭代策略相结合的方法得到较好的全局运动估计结果.六边形匹配算法由块搜索和迭代六边形匹配两步组成,同所有的基于模型的运动估计技术一样,六边形匹配算法也存在运算量大的问题难于满足实时编码需要.本文对六边形匹配算法从三个方面进行改进:用四步搜索替代全搜索、快速的纹理映射技术和有效的节点排序减少迭代次数.改进后的算法在基本上保证原算法的运动估计效率的情况下,算法的复杂度有明显的降低,使得基于模型的编码技术向实用化前进了一步.     

改进的四叉树多分辨率的形状编码算法

形状编码是MPEG-4不同于其它标准(如MPEG—1和MPEG-2等)的新特点。本文提出改进的四叉树多分辨率的形状编码的算法,文中详细探讨了对原基于四叉树多分辨率形状编码算法改进部分:帧内边界宏块采用基于相关系数准则的四叉树编码,帧间边界宏块采用有效搜索区域的运动估计和运动补偿的四叉树编码。该算法用相关系数来控制四叉树的复杂度,客观视觉质量下降很少,而改进的四叉树的节点数明显下降,进一步提高形状编码效率。二值形状的帧间编码时,运动估计仅在有效搜索区域进行,减少搜索次数,极大提高搜索效率。处理结果表明与原四叉树的形状编码相比, 性能上更具竞争力。     

基于反馈选择的P2P网络搜索算法

利用历史查询的搜索反馈结果,考虑节点兴趣,计算邻居节点的预计搜索成功性,动态选择转发的邻居节点,以减少消息数量和发现第一个文件的时间以及增加文件的查全率,提出一种基于反馈选择的P2P网络搜索算法。该算法通过计算邻居节点的动态有利度,定期调整邻居节点,提高了P2P网络的搜索稳定性。算法分析和实验结果表明,该算法的性能优于NS搜索算法。     

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.     

用可变形模板进行基于内容的图像分割算法

 本文提出一种采用可变形模板匹配技术进行基于内容的图像分割算法.通过预先计算出可变形模板沿着变形的正交曲线,并对模板曲线及正交曲线进行离散抽样,建立一基于正交曲线的二维(2-D)可变形模板,针对图像分割问题定义控制可变形模板进行变形的内、外部能量函数,本文采用遗传算法搜索能量函数最小的全局最优解.该新算法比传统的可变形模板匹配方法降低了搜索空间的维数,减少了算法对模板初始位置的敏感.对实际图像及模拟低信噪比图像处理的结果表明,新算法具有良好的分割精度及稳定性.     

运动估计中对不规则块匹配算法的理论研究

根据基于对象的MPEG-4视频标准，以对象宏块节点的运动矢量作类似的多边形匹配，提出了一种可变形块匹配方法。该方法在图像运动复杂的情况下能有效的保持图像的质量。     

针对硬件实现的整像素运动预测快速算法

一般基于预测的算法采用块匹配算法来消除相继帧简单冗余.通常,基于块的运动估计快速搜索算法采用的是减少搜索点的快速算法,文中介绍了一种适用于窄带低码率活动图像的帧间预测编码方法.根据H.264标准中的算法和编码方案,提出了一种帧间预测的硬件实现架构.运动预测完全针对亮度分量,采用基于中心预测和中途截止的快速搜索,给出了搜索窗结构.最后对其编码效果进行了分析.     

可重构分组密码逻辑阵列加权度量模型及高能效映射算法

针对基于粗粒度可重构阵列结构的分组密码算法映射情况复杂、难以实现统一度量的问题,该文采用多目标决策手段,以性能及功耗参数为决策目标,基于分组密码算法轮运算及粗粒度可重构阵列结构特征约束,提出了一种面向分组密码算法映射的加权度量模型.同时,采用主客观综合分析法,定义了模型权重参数的计算方式,从而通过配置合理的权重参数,以高能效映射算法实现差异化的映射.为了降低决策时间,该文进一步提出了基于二进制编码的枚举搜索算法,实现了最优映射结果搜索与映射矩阵建立的并行,使决策的时间复杂度降至O（2ⁿ）.实验结果表明,该文提出的加权度量模型能实现高效的分组密码算法映射方案决策,单位面积性能提升了约14.2%,能效提升了约一倍.     

基于加权流关系的流程相似性算法

为了提高从企业模型库中检索模型的效率,提出一种基于加权流关系的相似性算法,记作WF.首先,本文设计加权流关系集的生成算法,生成算法通过广度优先搜索方法遍历除输出库所外的所有节点及流关系,为每个流关系唯一分配权重,生成加权流关系集;其次,WF算法基于加权流关系集计算出加权变迁集,并依据WF相似性定义计算相似性;最后,通过实验及算法的时间复杂度分析比较表明：算法是正确且唯一确定结果的,且WF算法具有良好的性能.     

Fast Multi-reference Motion Estimation via Enhanced Downhill Simplex Search

Block motion estimation can be regarded as a function minimization problem in a finite-dimensional space. Therefore, fast block motion estimation can be achieved by using an efficient function minimization algorithm instead of a predefined search pattern, such as diamond search. Downhill simplex search is an efficient derivative-free function minimization algorithm. In this paper, we proposed a fast block motion estimation algorithm based on applying the downhill simplex search for function minimization. Several enhanced schemes are proposed to improve the efficiency and accuracy, including a new initialization process, a special rounding scheme, and an early-stop error function evaluation procedure. We also extend the downhill simplex search for the multi-reference frame motion estimation problem. Experimental results show superior performance of the proposed algorithm over some existing fast block matching methods on several benchmarking video sequences.     

Color Texture Segmentation Based on the Modal Energy of Deformable Surfaces

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.     

基于整数小波变换的嵌入式零树编码算法

在讨论整数小波变换的基础上,分析了嵌入式零树小波算法,针对EZW算法存在的一些问题,提出了一种基于整数小波变换的嵌入式零树编码算法。讨论了该算法考虑的几点因素的改进方法,详细研究了该算法的搜索过程和流程,并利用一个序列进行测试,测试结果表明,EZIWT算法能够提高压缩率,减少时间复杂度,低比特率时更明显。     

Deformable shape finding with models based on kernel methods.

In this paper, a new kernel-based deformable model is proposed for detecting deformable shapes. To incorporate valuable information for shape detection, such as edge orientations into the shape representation, a novel scheme based on kernel methods has been utilized. The variation model of a deformable shape is established by a set of training samples of the shape represented in a kernel feature space. The proposed deformable model consists of two parts: a set of basis vectors describing the sample subspace, including the shape representations of the training samples, and a feasibility constraint generated by the one-class support vector machine to describe the feasible region of the training samples in the sample subspace. The aim of the proposed feasibility constraint is to avoid finding some invalid shapes. By using the proposed deformable model, an efficient algorithm without initial solutions is developed for shape detection. The proposed approach was tested against real images. Experimental results show the effectiveness of the proposed deformable model and prove the feasibility of the proposed approach.     

Optimized hierarchical block matching for fast and accurate image registration

Recently the camera resolution has been highly increased, and the registration between high-resolution images is computationally expensive even by using hierarchical block matching. This paper presents a novel optimized hierarchical block matching algorithm in which the computational cost is minimized for the scale factor and the number of levels in the hierarchy. The algorithm is based on a generalized version of the Gaussian pyramid and its inter-layer transformation of coordinates. The search window size is properly determined to resolve possible error propagation in hierarchical block matching. In addition, we also propose a simple but effective method for aligning colors between two images based on color distribution adjustment as a preprocessing. Simplifying a general color imaging model, we show much of the color inconsistency can be compensated by our color alignment method. The experimental results show that the optimized hierarchical block matching and color alignment methods increase the block matching speed and accuracy, and thus improve image registration. Using our algorithm, it takes about 1.28 s for overall registration process with a pair of images in 5 mega-pixel resolution.