用于快速块运动预测的多方向钻石搜索算法

传统的钻石搜索算法只考虑块误差梯度下降最大的方向,从而降低了得到最佳匹配点为全局最优的概率.在钻石搜索算法的基础上,提出了多方向钻石搜索算法,用于实现视频编码中的块运动估计.该算法考虑所有的块误差梯度下降的方向,而不是只考虑梯度下降最大的那个方向,在钻石搜索法的第一步之后,对LDSP(Large Diamond Search Pattern,LDSP)外围8个点中小于中心点BDM(Block Distortion Measure,BDM)的方向上执行搜索.与全搜索、新三步搜索、四步搜索、基于块的梯度下降搜索、钻石搜索算法相比较,实验结果表明该算法在搜索速度和搜索质量上性能较好.     

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.     

一种有效的三步运动估计算法

为了减小运动估计算法的计算复杂度和提高红外图像超分辨率重建的可靠性,提出了一种有效的三步搜索算法.该算法采用多步搜索策略,根据运动矢量分布的中心偏移性及并行处理的思想,在最佳匹配点所在的区域使用菱形小模板代替新三步法中的正方形小模板来进行精细搜索,以提高算法的搜索精度.实验结果表明,与全搜索法、菱形搜索算法和预测性菱形搜索算法相比,该算法能在保证搜索精度的同时大大缩短消耗时间.     

一种快速的半像素运动矢量搜索算法

提出了一种基于梯度预测的快速半像素运动矢量搜索算法.实验结果表明,在H.263编码器中使用该算法的运算量,比在相同量化阶下的半像素运动矢量搜索算法下降45%,并且图像的PSNR和码率变化很小.该算法可以很容易地应用到H.264的1/4像素运动矢量搜索中.     

一种用于视频编码的运动估计新算法

提出一种新的改进三步搜索法(NITSS),该方法充分利用视频序列运动矢量在空间分布上的中心偏置特性,在三步搜索算法的基础上引入了以"×"字型分布的5个点构成的搜索点群,并通过同一帧相邻宏块的运动矢量之间存在的空间相关性来预测初始运动矢量,用来加强中心偏置特性.仿真结果表明,同TSS算法相比,NITSS算法降低了搜索运算量,提高了搜索精度.     

一种起点预测的十字形快速运动估计算法

基于时运动矢量概率分布的方向性中心偏移特性和起点预测搜索技术的研究,提出了一种起点预测的十字形快速运动估计算法(IPPAFA),在保持搜索精度的同时有效降低了计算复杂度。实验结果表明,在图像质量几乎不下降的情况下,与钻石搜索算法(DS)和运动矢量场自适应搜索算法(MVFAST)相比,该算法在速度和准确性方面都优于传统的快速运动估计算法。     

一种改进的分形图像压缩算法

为了提高分形图像压缩编码的速度,针对在基本分形图像压缩算法中值域块编码匹配搜索时需要对变换后的定义域块一一对应,导致编码时间较长的缺点,提出了一种基于菱形搜索算法的分形图像压缩编码新算法.菱形搜索算法是一种运动估计的快速搜索算法,主要过程是在所有的候选块中搜索当前块的最优匹配块.通过运用菱形搜索算法中的大小菱形模板进行匹配搜索,实验证明文中算法在提高编码速度和降低编码复杂度是有效的.     

基于H.264待编码宏块纹理特性的快速搜索算法

为减小H.264帧间预测运动估计算法复杂度,提出了一种基于待编码宏块纹理特性以排除部分搜索位置的方法.该方法可以大大减少待搜索位置的数目,缩短帧间预测时间.同时给出了一种基于宏块纹理的自适应快速搜索算法.实验表明,新算法在重建图像质量没有明显下降的前提下,编码速度有所提高.     

一种基于H.264的快速运动估计算法

H.264是现有最重要数据压缩编码国际标准之一。同时快速运动估计算法一直是视频压缩中的研究热点。本文针对一些快速估计算法过早确定了搜索方向,容易陷入局部最小点,损失了搜索精度的情况,在原有基础上,运用菱形十字搜索算法(DCS)与阈值估计相结合的方法提高了运动估计的性能。实验结果表明,该算法可以用较小的搜索代价取得与全搜索(FS)相当的效果,并且在搜索速度方面优于钻石法(DS)。     

视频编码中的块运动估计算法分析（三）

4．6动态搜索窗调整算法（DSWA）通常运动矢量在搜索区域内具有中心偏置分布特性．而3SS算法在搜索区域为（±16．±16）时第一步搜索采用17×17的搜索窗，对运动块的运动矢量估计来说初始步长太大．跳出了可能性比较大的区域．导致搜索方向的不确定性．故而有可能陷于局部最小。动态搜索窗调整算法在搜索过程中可以根据搜索窗收敛因子的大小自动调整下一个搜索阶段的搜索窗的尺寸，是一种自适应的梯度搜索算法。DSWA算法的搜索窗收敛因子Rw为当前阶段搜索窗大小与上一阶段搜索富大小之比．可以表示为T和H是常数参量．是通过统计一些…     

A new diamond search algorithm for fast block-matching motionestimation

Based on the study of motion vector distribution from several commonly used test image sequences, a new diamond search (DS) algorithm for fast block-matching motion estimation (BMME) is proposed in this paper. Simulation results demonstrate that the proposed DS algorithm greatly outperforms the well-known three-step search (TSS) algorithm. Compared with the new three-step search (NTSS) algorithm, the DS algorithm achieves close performance but requires less computation by up to 22% on average. Experimental results also show that the DS algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.     

Optimum block-adaptive learning algorithm for errorback-propagation networks

An optimum block-adaptive learning rate (OBALR) backpropagation (BP) algorithm for training feedforward neural networks with an arbitrary number of neuron layers is described. The algorithm uses block-smoothed gradient as direction for descent and no momentum term, but produces an optimum block-adaptive learning rate which is constant within each block and is updated adaptively at the beginning of each block iteration so that it is kept optimum in a sense of minimizing the approximate output mean-square error of the block. Several computer simulations were tested on learning a deterministic chaos time-series mapping. The OBALR BP algorithm not only overcame the difficulty in choosing good values of the two parameters, but also provided significant improvement on learning speed and descent capability over the standard BP algorithm     

一种结合遗传算法和钻石搜索的多模式快速运动估计方法

为了解决视频编码中运动矢量搜索精度与速度的矛盾,本文提出了一种基于遗传算法(GA)和钻石搜索(DS)的多模式快速运动估计方法——MMS算法.它以图像序列的时空预测矢量作为图像活动剧烈程度的判据,自适应选择搜索模式.针对平缓运动类型使用快速的DS搜索模式,针对剧烈运动类型使用GA/DS联合搜索模式.与现有的次优解快速算法相比,MMS有效地解决了在大运动矢量情况下编码器性能下降的问题,可以从整体上提升编码器的性能,接近理想的全搜索法的结果;与其它直接利用GA进行全局优化的方法相比,MMS利用DS配合GA实现加速收敛.此外,通过引入多模式处理的概念,在保证搜索精度的同时,充分发挥了次优解算法的效率,整体编码速度与DS等快速算法的结果十分接近.这一方法为有效地解决运动估计中的矛盾问题提供了一个新的处理框架.实验结果验证了算法的性能.     

基于梯度下降法的ISAR最小熵相位校正算法

最小熵相位校正算法是一种非常有潜力的ISAR相位校正算法,有多种不同的方法可以实现最小熵相位校正算法,如定点迭代法即是一种典型的最小熵相位校正算法.文中运用梯度下降法来实现最小熵相位校正的方法,并且通过对实测数据的ISAR成像,将梯度下降法与其他方法的收敛速度进行了比较,同时讨论了如何对梯度下降法步长参数进行选择.     

Evolutionary fuzzy neural networks for hybrid financial prediction

In this paper, an evolutionary fuzzy neural network using fuzzy logic, neural networks (NNs), and genetic algorithms (GAs) is proposed for financial prediction with hybrid input data sets from different financial domains. A new hybrid iterative evolutionary learning algorithm initializes all parameters and weights in the five-layer fuzzy NN, then uses GA to optimize these parameters, and finally applies the gradient descent learning algorithm to continue the optimization of the parameters. Importantly, GA and the gradient descent learning algorithm are used alternatively in an iterative manner to adjust the parameters until the error is less than the required value. Unlike traditional methods, we not only consider the data of the prediction factor, but also consider the hybrid factors related to the prediction factor. Bank prime loan rate, federal funds rate and discount rate are used as hybrid factors to predict future financial values. The simulation results indicate that hybrid iterative evolutionary learning combining both GA and the gradient descent learning algorithm is more powerful than the previous separate sequential training algorithm described in.     

An efficient search strategy for block motion estimation usingimage features

Block motion estimation using the exhaustive full search is computationally intensive. Fast search algorithms offered in the past tend to reduce the amount of computation by limiting the number of locations to be searched. Nearly all of these algorithms rely on this assumption: the mean absolute difference (MAD) distortion function increases monotonically as the search location moves away from the global minimum. Essentially, this assumption requires that the MAD error surface be unimodal over the search window. Unfortunately, this is usually not true in real-world video signals. However, we can reasonably assume that it is monotonic in a small neighborhood around the global minimum. Consequently, one simple strategy, but perhaps the most efficient and reliable, is to place the checking point as close as possible to the global minimum. In this paper, some image features are suggested to locate the initial search points. Such a guided scheme is based on the location of certain feature points. After applying a feature detecting process to each frame to extract a set of feature points as matching primitives, we have extensively studied the statistical behavior of these matching primitives, and found that they are highly correlated with the MAD error surface of real-world motion vectors. These correlation characteristics are extremely useful for fast search algorithms. The results are robust and the implementation could be very efficient. A beautiful point of our approach is that the proposed search algorithm can work together with other block motion estimation algorithms. Results of our experiment on applying the present approach to the block-based gradient descent search algorithm (BBGDS), the diamond search algorithm (DS) and our previously proposed edge-oriented block motion estimation show that the proposed search strategy is able to strengthen these searching algorithms. As compared to the conventional approach, the new algorithm, through the extraction of image features, is more robust, produces smaller motion compensation errors, and has a simple computational complexity.     

A fully adaptive normalized nonlinear gradient descent algorithm for complex-valued nonlinear adaptive filters

A fully adaptive normalized nonlinear complex-valued gradient descent (FANNCGD) learning algorithm for training nonlinear (neural) adaptive finite impulse response (FIR) filters is derived. First, a normalized nonlinear complex-valued gradient descent (NNCGD) algorithm is introduced. For rigour, the remainder of the Taylor series expansion of the instantaneous output error in the derivation of NNCGD is made adaptive at every discrete time instant using a gradient-based approach. This results in the fully adaptive normalized nonlinear complex-valued gradient descent learning algorithm that is suitable for nonlinear complex adaptive filtering with a general holomorphic activation function and is robust to the initial conditions. Convergence analysis of the proposed algorithm is provided both analytically and experimentally. Experimental results on the prediction of colored and nonlinear inputs show the FANNCGD outperforming other algorithms of this kind.     

Jointly multi‐user detection and channel estimation with genetic algorithm

This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multi‐user channel estimation (MuChE) and detection problems at its maximum‐likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi‐user detection (MuD) show that the proposed genetic algorithm multi‐user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi‐user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near‐optimum multi‐user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi‐user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE–GAMuD scheme can be regarded as a promising alternative for implementing third‐generation (3G) and fourth‐generation (4G) wireless systems in the near future. Copyright © 2010 John Wiley & Sons, Ltd.     

Corrections to `Gradient algorithms for designing predictive vectorquantizers'

An error in a paper by Chang and Gray (see ibid., vol.34, no.4, p.679-90, 1986) is pointed out and corrected. The error invalidates their observation that the generalized Lloyd algorithm is a gradient descent technique but the generalized Lloyd algorithm is a member of the related class of coordinate descent techniques. Convergence rate analysis of gradient descent algorithms for vector quantizer design is provided