无链表SPIHT图像压缩算法的FPGA实现

SPIHT图像压缩方案是一种实用高性能图像压缩编码算法,但算法中使用的三个链表使其很难在FPGA中高速实现.而无链表SPIHT是对原SPIHT算法的改进,无链表SPIHT图像压缩算法使用标志位来取代链表,严格执行宽度优先的搜索策略,因此压缩性能比深度优先的搜索策略要好且能在FPGA中高速实现.为此,本文在深入研究无链表SPIHT图像压缩算法的基础上,设计了算法的FPGA实现结构.实验结果表明,该FPGA实现方案可以应用到高速实时图像压缩系统中.     

一种易于硬件实现的小波图像压缩算法仿真

SPIHT算法是压缩效率很高的静止图像压缩编码算法,但需要使用三个集合链表来存储已编码信息,内存需求大及难于硬件实现。据此,提出一种改进的易于硬件实现的小波图像压缩算法。该算法引入了线性索引技术和LZC算法标志位图,采用了类似于SPIHT算法的扫描顺序,将待编码流按不同种类进行算术编码。仿真实验结果表明,该算法的峰值信噪比优于LZC算法,略低于SPIHT算法,但对内存需求仅为SPIHT算法的二十分之一,易于硬件实现。     

改进的无链表零树编码算法

通过对SPIHT算法和LZC算法的研究,提出一种改进的无链表零树编码算法。该算法改进了树编码过程,降低了编码复杂度,易于硬件实现,并且用小波系数的最高比特位来存储标志图,使得编码所需内存进一步减少。实验结果表明在相同的压缩比下,新算法重建图像的峰值信噪比明显优于LZC算法,而仅比SPIHT算法有略微的降低。     

一种改进的无链表小波零树编码算法

提出一种对LZC算法作了改进的图像压缩编码方法。该方法在编解码的过程中都省去了链表，大大减少了内存消耗，降低了复杂度，提高了编码速度。实验结果表明，该算法在性能上比LZC算法提高约0．7dB左右，和SPIHT算法相比压缩性能近似但硬件更易实现。     

基于改进SPIHT的静态图像编码

本文在零树编码算法分析的基础上,针对SPIHT算法的不足,提出了一种新的零树编码算法。该算法以SPIHT为基础,改进了其零树结构及编码流程,同时引入LZC算法的标志位图思想,在保证恢复图像质量的前提下,降低了内存消耗,提高了编解码速度。仿真实验结果表明,相对于SPIHT算法而言,重构图像的峰值信噪比和直观图像质量,本文算法都表现出了优良的性能,尤其是在低比特率下表现跟明显。     

一种改进的基于零树编码的小波压缩图像算法

在SPIHT算法的基础上,提出一种改进的基于零树编码的小波压缩图像编码算法。基本思想是综合了FrederickW.Wheeler和WilliamA.Pearlman提出的线性索引技术及LZC采用的标志位图,采用了与SPIHT类似的扫描顺序,与SPIHT不同的是,本文算法不用列表,硬件实现非常容易。最后经实验证明,本文算法的峰值信噪比仅比SPIHT算法略低,而对内存空间的要求则大大减少,因此在几乎没有降低恢复图像质量的前提下,使零树编码算法的硬件实现成为可能。     

适用于硬件实现的零树编码算法

在Listless Zerotree Coding(LZC)算法的基础上提出了一种内存空间更小的静止图像零树编码算法，该算法与SPIHT等经典的零树编码算法的区别在于编解码过程中不使用列表结构，与LZC算法相比则节约了两个位图(Bit Map)，而这些位图为编码算法提供了重要系数和重要后代集合的位置信息．实验结果证明，本文的算法在不降低图像度量的前提下大大降低了零树编码算法的内存要求，有利于硬件实现．     

一种无链表零树熵编码算法及其FPGA实现

传统熵编码算法复杂度高,且硬件实现较难。为此,提出一种易于FPGA硬件实现的无链表零树图像编码改进算法,该算法结合LZC和SPIHT算法的特点,增加了对孙子节点的判断,克服SPIHT算法中内存消耗大、复杂度高以及LZC算法中图像重建质量下降等缺点。采用该算法的编码器在对任意尺寸图像进行5层小波变换时,所需缓存固定为1 536 bit,远小于SPIHT算法。在相同比特率的情况下,图像重建质量PSRN值比LZC算法平均提高约0.7 dB。在Xilinx Virtex4 LX100硬件平台上进行实验,结果表明,当时钟频率为100 MHz、压缩率为1/40时,该算法处理1 600×1 200×24 bit图像的速度可达20 f/s。     

一种改进的LZC图像编码算法

摘要提出了一种改进的LZC(1istlesszerotreecoding)图像编码算法，该算法不仅继承了原有LZC算法不使用链表、易于硬件实现的优点，而且针对原始算法子带扫描顺序不尽合理、递归结构算法复杂度较高等缺点，通过改变空间子带的编码顺序，将块的概念引入编码过程在改进算法中完全采用顺序结构等措施进一步提高了原有算法的工作效率、降低了LZC算法的复杂度。实验结果表明，该算法是一种高效的图像编码算法，其不仅具有良好的视觉效果、无方块效应，而且在相同压缩比情况下，该算法的图像重构质量和PSNR均明显优于原始的LZC算法。     

一种小波图像压缩编码的抗干扰算法

提出了一种基于小波树搜索方式的改进了的小波图像压缩算法,算法保留和继承了EZW和SPIHT算法的所有优点,改进了它们难于抵御干扰的弱点,重点探讨了搜索规则、格点矢量编码方法以及责任中结命令 道纠错的沸合编码选择,并给出了在模拟的噪声环境下,用该算法进行编解的峰什玲噪比及其与SPIHT算法的比较。     

Efficient algorithms to create and maintain balanced and threaded binary search trees

The algorithm proposed by Chang and lyengar to perfectly balance binary search trees has been modified to not only balance but also thread binary search trees. Threads are constructed in the same sequence as normal pointers during the balancing process. No extra workspace is necessary, and the running time is also linear for the modified algorithm. Such produced tree structure has minimal average path length for fast information retrieval, and threads to facilitate more flexible and efficient traversing schemes. Maintenance and manipulation of the data structure are discussed and relevant algorithms given.     

Considering complex training images with search tree partitioning

Using a complex training image (TI) for the single normal equation simulation (SNESIM) algorithm results in poor simulated realizations since that image contains trends and location specific patterns. By pooling all the TI patterns in a single search tree and not recording the relative locations of those patterns, some critical features of these complex TIs are lost.The search tree partitioning approach subdivides a large TI into imbricated, homogeneous, smaller images, called partition classes. Each of these partition classes has a corresponding search tree that can be utilized by the SNESIM algorithm. These partition classes are obtained by processing the TIs with spatial filters that are pattern sensitive. The resulting filter scores are then clustered into partition classes. All patterns within a partition class are recorded by a search tree; there is one tree per partition class. At each pixel along the simulation path, the partition class is retrieved first and used to select the appropriate search tree. That search tree contains the patterns relevant to that partition class.In practice, the partitioning approach adds flexibility in choosing a TI. TIs that were easier to obtain but traditionally too complex for simulation can now be considered as input to SNESIM. In many cases, it also significantly increases the simulation speed by searching a vector of smaller trees instead of a single large one. A plugin for the SGeMS software is provided.     

基于改进决策树算法的网络关键资源页面判定

关键资源页面是网络信息环境中一种重要的高质量页面,是用户进行网络信息检索的主要目标.决策树算法是机器学习中应用最广的归纳推理算法之一,适用于关键资源页面的判定.然而由于Web数据均一采样的困难性,算法缺乏有足够代表性的反例进行训练.为了解决这个问题,提出一种利用训练样例的统计信息而非个体信息进行学习的改进决策树算法,并利用这种算法实现了独立用户查询的关键资源页面判定.在2003年文本信息检索会议(Text Retrieval Confefence,简称TREC)标准的评测条件下,基于此种改进决策树算法的大规模网络信息检索实验获得了超过基本算法40%的性能提高.这不仅提供了一种查找Web关键资源页面的有效方式,也给出了提高决策树算法性能的一个可行途径.     

Efficient search algorithm on compact S-trees

Based on the S-tree, the modified S-tree is a newly proposed spatial data structure for representing digital binary images, which can support fast search and query in pictorial database. In this paper, we first present a new spatial data structure called the compact S-tree to represent binary images, which uses less memory when compared to the modified S-tree. We then present a fast search algorithm on the compact S-tree. Further, the application to neighbor finding is also investigated. Experimental results show that the proposed search algorithm is faster than the previous known results on the S-tree and the modified S-tree.     

GSST: anytime guaranteed search

We present Guaranteed Search with Spanning Trees (GSST), an anytime algorithm for multi-robot search. The problem is as follows: clear the environment of any adversarial target using the fewest number of searchers. This problem is NP-hard on arbitrary graphs but can be solved in linear-time on trees. Our algorithm generates spanning trees of a graphical representation of the environment to guide the search. At any time, spanning tree generation can be stopped yielding the best strategy so far. The resulting strategy can be modified online if additional information becomes available. Though GSST does not have performance guarantees after its first iteration, we prove that several variations will find an optimal solution given sufficient runtime. We test GSST in simulation and on a human-robot search team using a distributed implementation. GSST quickly generates clearing schedules with as few as 50% of the searchers used by competing algorithms.     

Randomized splay trees: Theoretical and experimental results

Splay trees are self-organizing binary search trees that were introduced by Sleator and Tarjan [J. ACM 32 (1985) 652-686]. In this paper we present a randomized variant of these trees. The new algorithm for reorganizing the tree is both simple and easy to implement. We prove that our randomized splaying scheme has the same asymptotic performance as the original deterministic scheme but improves constants in the expected running time. This is interesting in practice because the search time in splay trees is typically higher than the search time in skip lists and AVL-trees. We present a detailed experimental study of our algorithm. On request sequences generated by fixed probability distributions, we can achieve improvements of up to 25% over deterministic splaying. On request sequences that exhibit high locality of reference, the improvements are minor.     

A Tree-based Mergesort

We demonstrate that if standard Mergesort is implemented using finger search trees instead of arrays it optimally adapts to a set of measures of presortedness not fulfilled by any other algorithm. Received: 7 October 1993 / 18 July 1996     

一种高效的图像数据库检索方法

对于大型图像库,如何高效地检索出相似图像是图像检索系统的一大挑战。提出了一种改进的K-均值聚类算法建立分层结构的索引,再利用A*树算法和三角不等式原则及N近邻方法对索引库快速高效地搜索,达到对图像库快速高效检索相似图像的目的。实验在Corel图像库上进行,实验结果表明该方法以对数时间复杂度实现基于内容的高效检索。     

A novel semi-fragile forensic watermarking scheme for remote sensing images

A semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote-sensing images applying a tree-structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of the multi- or hyperspectral image is used to embed the mark into it in order to detect any significant modification of the original image. The image is segmented into three-dimensional blocks and a tree-structured vector quantizer (TSVQ) is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image.     

基于Hausdorff距离图象配准方法研究

图象配准是图象融合的一个重要步骤，为此提出了一种自动图象配准算法，该算法从两幅待配准的图象中分别抽取特征点，然后选用Hausdorff距离对两特征点集进行匹配，得到点集间的仿射变换，从而实现图象的自动配准，此算法以特征点而不是物体边缘计算仿射变换，大大降低了计算Hausdorff距离的运算量；同时，基于Hausdorff距离的图象匹配只需要点集之间的对应，而无须点与点的对应，因而可以使用于存在较大物体形变的情况，即完成两幅差异较大图象的配准，实验结果证明了算法的有效性。