Fractal compression coding based on wavelet transform with diamond search

In this paper, a fractal image compression coding scheme based on wavelet transform with diamond search is proposed. The goal is to offer fast positioning. According to search pattern and search path of diamond search, the proposed scheme just needs to search in the domain blocks in the fixed place around the range blocks. Our proposed method has benefits in reducing the search time and enhancing the coding speed compared with other image compression techniques.     

Fractal video sequences coding with region-based functionality

In this paper, we explore the fractal video sequences coding in the context of region-based functionality. Since the main drawback of fractal coding is the high computational complexity, some schemes are proposed to speed up the encoding process. As fractal encoding essentially spends most time on the search for the best-matching block in a large domain pool, this paper firstly ameliorates the conventional CPM/NCIM method and then applies a new hexagon block-matching motion estimation technology into the fractal video coding. The images in the video sequences are encoded region by region according to a previously-computed segmentation map. Experimental results indicate that the proposed algorithm spends less encoding time and achieves higher compression ratio and compression quality compared with the conventional CPM/NCIM method.     

Fast fractal image encoding using one-norm of normalised block

The baseline fractal image encoding with full search typically requires a very long encoding time, which is essentially spent on searching for the best-matched block to an input range block in a large domain pool. In this paper, one-norm of normalised block is first utilized to avoid the excessive search, in which the search process might be early terminated, and thus remaining domain blocks could be safely discarded. Then the encoding schemes proposed recently by the authors are used to further reduce the search space and improve the decoded image quality. Experiments show that, for three popular 512 × 512 test images, the proposed algorithm can averagely reduce the runtime by about 40 times while there is averagely the PSNR gain of 0.91 dB, in comparison with the baseline fractal algorithm. Besides, when combined with the kick-out condition and the zero contrast prediction proposed recently by Lai et al. [Lai CM, Lam KM, Siu WC. A fast fractal image coding based on kick-out and zero contrast conditions. IEEE Trans Image Process 2003:11;1398–403], the combined algorithm further reduced the runtime by about 10% while maintaining the same PSNR exactly as that of the proposed algorithm.     

Image magnification based on similarity analogy

Aiming at the high time complexity of the decoding phase in the traditional image enlargement methods based on fractal coding, a novel image magnification algorithm is proposed in this paper, which has the advantage of iteration-free decoding, by using the similarity analogy between an image and its zoom-out and zoom-in. A new pixel selection technique is also presented to further improve the performance of the proposed method. Furthermore, by combining some existing fractal zooming techniques, an efficient image magnification algorithm is obtained, which can provides the image quality as good as the state of the art while greatly decrease the time complexity of the decoding phase.     

Matching search in fractal video compression and its parallel implementation in distributed computing environments

Fractal video compression is a relatively new video compression method. Its attraction is due to the high compression ratio and the simple decompression algorithm. But its computational complexity is high and as a result parallel algorithms on high performance machines become one way out. In this study we partition the matching search, which occupies the majority of the work in a fractal video compression process, into small tasks and implement them in two distributed computing environments, one using DCOM and the other using .NET Remoting technology, based on a local area network consists of loosely coupled PCs. Experimental results show that the parallel algorithm is able to achieve a high speedup in these distributed environments.     

GLS coding based security solution to JPEG with the structure of aggregated compression and encryption

There exists close relation among chaos, coding and cryptography. All the three can be combined into a whole as aggregated chaos-based coding and cryptography (ATC) to compress and encrypt data simultaneously. In particular, image data own high redundancy and wide transmission and thereby it is well worth doing research on ATC for image, which is very helpful to real application.JPEG with high compression ratio has not provided security. If JPEG is incorporated into powerful cryptographic features, its application can be further extended. For this reason, in this paper, GLS coding as a special form of ATC, which attains synchronous compression and encryption, is used to modify JPEG and fill its gap. An image is first initialized using DCT, quantization and run-length coding in turn, just as JPEG. Then, it is encoded and encrypted simultaneously by utilizing GLS coding and binary keystream resulting from the chaotic generator. Results demonstrate that our scheme can not only achieve good compression performance but also resist known/chosen-plaintext attacks efficiently.     

一种分形图像编码的改进方法

在分析小波包变换和分形编码特点的基础上,先将图像进行小波包分解,对进一步细分的高频部分直接进行频域截断,对低频部分进行分形压缩.计算机模拟试验表明,上述方案与基本分形编码方法相比,在重建图像主观质量和运行时间上都显示出优越性.     

Test driving three 1995 genetic algorithms: New test functions and geometric matching

Genetic algorithms have attracted a good deal of interest in the heuristic search community. Yet there are several different types of genetic algorithms with varying performance and search characteristics. In this article we look at three genetic algorithms: an elitist simple genetic algorithm, the CHC algorithm and Genitor. One problem in comparing algorithms is that most test problems in the genetic algorithm literature can be solved using simple local search methods. In this article, the three algorithms are compared using new test problems that are not readily solved using simple local search methods. We then compare a local search method to genetic algorithms for geometric matching and examine a hybrid algorithm that combines local and genetic search. The geometric matching problem matches a model (e.g., a line drawing) to a subset of lines contained in a field of line fragments. Local search is currently the best known method for solving general geometric matching problems.     

基于视觉模型和整数余弦变换的数字水印算法

本文通过引入整数余弦变换与Hash函数方法相结合，在视觉模型框架下提出了一种新的数字水印算法。整数变换的引入，提高了运算速度和图像质量，视觉模型引入，使得水印算法抗JPEG压缩以及其他图像处理方法能力强；本文水印方案加密方法符合公开密码体制，具有高度安全特性。     

Fast Wavelet Transform for Toeplitz Matrices and Property Analysis

Fast wavelet transform algorithms for Toeplitz matrices are proposed in this paper. Distinctive from the well known discrete trigonometric transforms, such as the discrete cosine transform （DCT） and the discrete Fourier transform （DFT） for Toeplitz matrices, the new algorithms are achieved by compactly supported wavelet that preserve the character of a Toeplitz matrix after transform, which is quite useful in many applications involving a Toeplitz matrix. Results of numerical experiments show that the proposed method has good compression performance similar to using wavelet in the digital image coding. Since the proposed algorithms turn a dense Toeplitz matrix into a band-limited form, the arithmetic operations required by the new algorithms are O（N） that are reduced greatly compared with O（N log N） by the classical trigonometric transforms.     

Hybridizing local search algorithms for global optimization

In this paper, we combine two types of local search algorithms for global optimization of continuous functions. In the literature, most of the hybrid algorithms are produced by combination of a global optimization algorithm with a local search algorithm and the local search is used to improve the solution quality, not to explore the search space to find independently the global optimum. The focus of this research is on some simple and efficient hybrid algorithms by combining the Nelder–Mead simplex (NM) variants and the bidirectional random optimization (BRO) methods for optimization of continuous functions. The NM explores the whole search space to find some promising areas and then the BRO local search is entered to exploit optimal solution as accurately as possible. Also a new strategy for shrinkage stage borrowed from differential evolution (DE) is incorporated in the NM variants. To examine the efficiency of proposed algorithms, those are evaluated by 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005. A comparison study between the hybrid algorithms and some DE algorithms and non-parametric analysis of obtained results demonstrate that the proposed algorithms outperform most of other algorithms and their difference in most cases is statistically considerable. In a later part of the comparative experiments, a comparison of the proposed algorithms with some other evolutionary algorithms reported in the CEC2005 confirms a better performance of our proposed algorithms.     

A new modified panoramic UAV image stitching model based on the GA-SIFT and adaptive threshold method

This paper presents panoramic unmanned aerial vehicle (UAV) image stitching techniques based on an optimal Scale Invariant Feature Transform (SIFT) method. The image stitching representation associates a transformation matrix with each input image. In this study, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between the images. An improved Geometric Algebra (GA-SIFT) algorithm is proposed to realize fast feature extraction and feature matching work for the scanned images. The proposed GA-SIFT method can locate more feature points with greater accurately than the traditional SIFT method. The adaptive threshold value method proposed solves the limitation problem of high computation load and high cost of stitching time by greater feature points extraction and stitching work. The modified random sample consensus method is proposed to estimate the image transformation parameters and to determine the solution with the best consensus for the data. The experimental results demonstrate that the proposed image stitching method greatly increases the speed of the image alignment process and produces a satisfactory image stitching result. The proposed image stitching model for aerial images has good distinctiveness and robustness, and can save considerable time for large UAV image stitching.     

求解柔性作业车间调度的动态群智能优化算法

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.     

A tabu search algorithm for solving economic lot scheduling problem

The economic lot scheduling problem has driven considerable amount of research. The problem is NP-hard and recent research is focused on finding heuristic solutions rather than searching for optimal solutions. This paper introduces a heuristic method using a tabu search algorithm to solve the economic lot scheduling problem. Diversification and intensification schemes are employed to improve the efficiency of the proposed Tabu search algorithm. Experimental design is conducted to determine the best operating parameters for the Tabu search. Results show that the tabu search algorithm proposed in this paper outperforms two well known benchmark algorithms.     

Inspired grey wolf optimizer for solving large-scale function optimization problems

Grey wolf optimizer algorithm was recently presented as a new heuristic search algorithm with satisfactory results in real-valued and binary encoded optimization problems that are categorized in swarm intelligence optimization techniques. This algorithm is more effective than some conventional population-based algorithms, such as particle swarm optimization, differential evolution and gravitational search algorithm. Some grey wolf optimizer variants were developed by researchers to improve the performance of the basic grey wolf optimizer algorithm. Inspired by particle swarm optimization algorithm, this study investigates the performance of a new algorithm called Inspired grey wolf optimizer which extends the original grey wolf optimizer by adding two features, namely, a nonlinear adjustment strategy of the control parameter, and a modified position-updating equation based on the personal historical best position and the global best position. Experiments are performed on four classical high-dimensional benchmark functions, four test functions proposed in the IEEE Congress on Evolutionary Computation 2005 special session, three well-known engineering design problems, and one real-world problem. The results show that the proposed algorithm can find more accurate solutions and has higher convergence rate and less number of fitness function evaluations than the other compared techniques.     

3-D mesh sequence compression using wavelet-based multi-resolution analysis

In this paper, we present two compression methods for irregular three-dimensional (3-D) mesh sequences with constant connectivity. The proposed methods mainly use an exact integer spatial wavelet analysis (SWA) technique to efficiently decorrelate the spatial coherence of each mesh frame and also to adaptively transmit mesh frames with various spatial resolutions. To reduce the temporal redundancy, the first proposed method applies multi-order differential coding (MDC) to the temporal sequences obtained from SWA. MDC determines the optimal order of the differential coder by analyzing the variance of prediction errors. Comparing with the first order differential coding (FDC) scheme, the method can improve the compression performance. The second proposed method applies temporal wavelet analysis (TWA) to the temporal sequences. In particular, this method offers spatio-temporal multi-resolution coding. Through simulations, we prove that our methods enable efficient lossy-to-lossless compression for 3-D mesh sequences in a single frame work.     

Fast Multilevel CVT-Based Adaptive Data Visualization Algorithm

<正>Efficient data visualization techniques are critical for many scientific applications. Centroidal Voronoi tessellation(CVT) based algorithms offer a convenient vehicle for performing image analysis,segmentation and compression while allowing to optimize retained image quality with respect to a given metric.In experimental science with data counts following Poisson distributions,several CVT-based data tessellation algorithms have been recently developed.Although they surpass their predecessors in robustness and quality of reconstructed data,time consumption remains to be an issue due to heavy utilization of the slowly converging Lloyd iteration.This paper discusses one possible approach to accelerating data visualization algorithms.It relies on a multidimensional generalization of the optimization based multilevel algorithm for the numerical computation of the CVTs introduced in[1],where a rigorous proof of its uniform convergence has been presented in 1-dimensional setting.The multidimensional implementation employs barycentric coordinate based interpolation and maximal independent set coarsening procedures.It is shown that when coupled with bin accretion algorithm accounting for the discrete nature of the data,the algorithm outperforms Lloyd-based schemes and preserves uniform convergence with respect to the problem size.Although numerical demonstrations provided are limited to spectroscopy data analysis,the method has a context-independent setup and can potentially deliver significant speedup to other scientific and engineering applications.     

An ADMM algorithm for second-order TV-based MR image reconstruction

In this paper, we propose a new model for MR image reconstruction based on second order total variation ( \(\text {TV}^{2}\) ) regularization and wavelet, which can be considered as requiring the image to be sparse in both the spatial finite differences and wavelet transforms. Furthermore, by applying the variable splitting technique twice, augmented Lagrangian method and the Barzilai-Borwein step size selection scheme, an ADMM algorithm is designed to solve the proposed model. It reduces the reconstruction problem to several unconstrained minimization subproblems, which can be solved by shrinking operators and alternating minimization algorithms. The proposed algorithm needs not to solve a fourth-order PDE but to solve several second-order PDEs so as to improve calculation efficiency. Numerical results demonstrate the effectiveness of the presented algorithm and illustrate that the proposed model outperforms some reconstruction models in the quality of reconstructed images.     

基于PGH矩改进SURF算法的工业散料识别研究

针对工业散料识别过程中图像特征匹配率低的问题,提出一种基于PGH矩的改进SURF图像匹配算法.首先,研究Gaussian-Hermite矩,将其扩展到复数空间中,推导出Polar-Gaussian-Hermite矩;其次,利用升降算符法计算图像PolarGaussian-Hermite矩,获得新的特征向量;最后,将原始图像提取的特征点和模板图像进行准确匹配得到最优匹配结果.实验结果表明算法能够解决缩放、旋转和曝光情况下工件匹配问题,误匹配率8%左右,满足工业散料识别系统的准确性和实时性要求.     

Greedy Algorithms for On-Line Data Compression

We consider on-line text-compression problems where compression is done by substituting substrings according to some fixed static dictionary (code book). Due to the long running time of optimal algorithms, several heuristics have been introduced in the literature. In this paper, we continue the investigations of3. We complete the worst-case analysis of the longest matching algorithm and of the differential greedy algorithm for several types of special dictionaries and we derive matching lower and upper bounds for all variants of this problem.