基于Krawtchouk矩的自适应门限四叉树分形编码

编码时间过长是分形图像压缩存在的主要问题,同时固定匹配门限影响着编码效率.本文首次将Krawtchouk矩引入到自适应分形编码中,提出了基于Krawtchouk矩的自适应门限四叉树分形编码方法.该方法计算图像块的12个Krawtchouk矩不变量,将这些矩不变量构成特征向量,并用这样的特征向量取代Range块和Domain块的灰度值矩阵进行匹配计算.文中给出了实验结果,并与基于四叉树的自适应门限分形图像IFS压缩方法进行了比较.结果表明,本文方法不仅大幅度减少了编码时间,而且同时也能提高图像恢复质量.     

一种基于显著不相关检验的快速分形图像编码方法

针对分形编码计算量过大的缺点,本文提出一种基于显著不相关检验的分形编码方法。对于文中的测试图像（Lenn256×256×8ppb和Boat256×256×8ppb）,其编码速度比基于子块分类的分形编码方法快2～15倍,而解码图像质量（PSNR）没有明显下降,压缩比还有一定的提高。     

一种基于显示不相关检验的快速分形图像编码方法

针对分形编码计算量过大的缺点,本文提出一种基于显著不相关检验的分形编码方法,对于文中的测试图像,其编码速度比基于子块分发形编码方法快２－１５倍,而解码图像质量ＰＳＮＲ）没有明显下降,压缩比还有一定的提高。     

一种基于K-均值聚类优化的快速分形图像压缩算法

提出了一种基于K-均值聚类的快速分形图像压缩算法,对搜索窗中的父块和子块,根据其方差的不同,用K-均值聚类方法分别对子块和父块进行聚类,子块只对同一类中的父块进行匹配,从而大大缩短了编码时间。实验结果表明,与经典分形压缩算法相比,本文算法编码速度可提高5倍;同基于方差的快速分形压缩算法相比,本文算法也有明显的优势。     

基于矢量量化的层次分形编码方法

文中提出了一种新的分形图像压缩方法,该方法将矢量量化的概念应用于分形块编码中,对图像的平缓区进行矢量量化的线性组合编码,对图像的丰富细节区用分形编码,并且在分形编码时,采取了层次处理。实验表明,与基本的分形块编码方法相比,本文提出的矢量量化层次分形编码方法在保证一定的重建图像质量下,使图像的压缩比有了明显的提高,并且大大提高了编码和解码速度。     

基于方差不变特性的快速分形图像编码方法

本文结合图像子块的自身统计特性，提出一种基于方差不变特性的快速分形编码方法。实验结果表明，该方法可使编码速度有较大提高，而解码图像质量无明显下降；且有相对确定的编码时间，有助于编码系统的硬件实现。     

基于区域分割的分形图像压缩编码方法

在基于块分割的分形图像压缩编码研究基础上，提出了一种基于区域分割和十字搜索模型的分形图像压缩编码新方法，实验结果表明，该方法与基于块分割的分形图像压缩编码方法相比，在保持恢复图像质量的前提下，压缩比和编码速度都有了显著的提高，是一种有效的分形图像压缩编码方法。     

基于子域对角和的快速分形图像编码算法

为了改进分形图像压缩编码过程耗时过长而影响实用的问题,新定义了子域对角和来描述图像块的特征.算法把码本按照子域对角和特征的大小排序,对每个待编码的Range块,仅在赋序码本中找到与Range块的子域对角和的数值最接近的Domain块,并在此Domain块的邻域内搜索最佳匹配块.实验结果表明,在保证解码图像质量的前提下,该算法较快地提高了编码速度.     

基于域块预搜索的快速分形图像编码方法

由于分形图像编码过程非常耗时,本文提出了一种加快编码速度的新方法。通过对分形编码过程中的域块进行分析,首次提出了相似度和最大相似度的概念,并建立了预搜索方法的理论基础,提出了一种基于域块预搜索的快速分形编码方法。实验结果显示,与原始的Jacquin方案相比,在峰值信噪比(PSNR)降低1-3dB时,编码速度可提高400-600倍。     

广播与电视

0310062基于相关系数的快速分形图象压缩方法[刊]/任卓敏//上海大学学报(自然科学版).—2002,8(6).—482～485(K)分形图象压缩的缺点在于编码时间过长。该文提出了一种基于相关系数的匹配判别式,可大量排除不相关的 Domain(区域)块,从而可降低匹配的计算量,提高编码的速度。判别式中阈值由全局的相关系数ρ和各 Range(排列)块的标准差σ_r 所确定。本方法在大幅提高编码速度的同时,能保证良好的图象质量,这得到了实验结果的验证。参5     

Image analysis by Krawtchouk moments

A new set of orthogonal moments based on the discrete classical Krawtchouk polynomials is introduced. The Krawtchouk polynomials are scaled to ensure numerical stability, thus creating a set of weighted Krawtchouk polynomials. The set of proposed Krawtchouk moments is then derived from the weighted Krawtchouk polynomials. The orthogonality of the proposed moments ensures minimal information redundancy. No numerical approximation is involved in deriving the moments, since the weighted Krawtchouk polynomials are discrete. These properties make the Krawtchouk moments well suited as pattern features in the analysis of two-dimensional images. It is shown that the Krawtchouk moments can be employed to extract local features of an image, unlike other orthogonal moments, which generally capture the global features. The computational aspects of the moments using the recursive and symmetry properties are discussed. The theoretical framework is validated by an experiment on image reconstruction using Krawtchouk moments and the results are compared to that of Zernike, pseudo-Zernike, Legendre, and Tchebyscheff moments. Krawtchouk moment invariants are constructed using a linear combination of geometric moment invariants; an object recognition experiment shows Krawtchouk moment invariants perform significantly better than Hu's moment invariants in both noise-free and noisy conditions.     

2D and 3D image localization,compression and reconstruction using new hybrid moments

In this article, we will present a new set of hybrid polynomials and their corresponding moments, with a view to using them for the localization, compression and reconstruction of 2D and 3D images. These polynomials are formed from the Hahn and Krawtchouk polynomials. The process of calculating these is successfully stabilized using the modified recurrence relations with respect to the n order, the variable x and the symmetry property. The hybrid polynomial generation process is carried out in two forms: the first form contains the separable discrete orthogonal polynomials of Krawtchouk–Hahn (DKHP) and Hahn–Krawtchouk (DHKP). The latter are generated as the product of the discrete orthogonal Hahn and Krawtchouk polynomials, while the second form is the square equivalent of the first form, it consists of discrete squared Krawtchouk–Hahn polynomials (SKHP) and discrete polynomials of Hahn–Krawtchouk squared (SHKP). The experimental results clearly show the efficiency of hybrid moments based on hybrid polynomials in terms of localization property and computation time of 2D and 3D images compared to other types of moments; on the other hand, encouraging results have also been shown in terms of reconstruction quality and compression despite the superiority of classical polynomials.

     

Speed-up in fractal image coding: comparison of methods

Fractal image compression has received much attention from the research community because of some desirable properties like resolution independence, fast decoding, and very competitive rate-distortion curves. Despite the advances made, the long computing times in the encoding phase still remain the main drawback of this technique. So far, several methods have been proposed in order to speed-up fractal image coding. We address the problem of choosing the best speed-up techniques for fractal image coding, comparing some of the most effective classification and feature vector methods-namely Fisher (1994), Hurtgen (1993), and Saupe (1995, 1996)-and a new feature vector coding scheme based on the block's mass center. Furthermore, we introduce two new coding schemes combining Saupe with Fisher, and Saupe with mass center coding scheme. Experimental results demonstrate both the superiority of feature vector techniques on classification and the effectiveness of combining Saupe and the mass center coding scheme, an approach that exhibits the best time-distortion curves.     

基于FGSE的快速分形图像编码算法及其多描述编码方案

本文在基于分形图像编码的多描述编码方案（MDFIC）的基础上,做了两处改进：一是引入提出的基于FGSE的快速分形图像编码算法,以提高编码速度;二是利用相邻值域块均值间的相关性,以减少比特率。实验结果表明,本方案与MDFIC相比,可以在保持解码图像质量几乎不变的情况下,提高编码速度和减少比特率,并具有较高的鲁棒性。     

多分类器融合的快速高维特征聚类图像分割

提出一种多分类器融合的快速高维特征聚类图像分割方法,将图像高维特征数据的分类分解为基于灰度(颜色)特征的最佳模糊分类以及基于空域约束的统计分类等多个低维特征数据的分类.通过多分类器融合的方法将不同分类器得到的分类结果进行优化整合,得到最后的分类结果.实验证明:与其它图像分类算法相比,该方法拥有更好的分割性能并大大提高了计算速度,最大限度地保证了分割算法计算的简单有效性.     

多波段遥感图像的快速分形编码

分形图像编码用动态迭代过程的参数来表示相应的静态图像数据,可以突破熵压缩编码的理论界限,因而得到广泛的关注.将分形图像编码应用于多波段遥感图像压缩中,根据多波段遥感图像的特点,对基于四叉树分割的快速分形编码方法进行改进,令各个波段图像共享同一四叉树分割方式,同时,利用多波段遥感图像的谱间相关性,缩小仿射变换的搜索匹配空间,以此来提高压缩比和编码速度,实验表明,本方法能显著提高基于四叉树分割的分形编码方法的性能,取得了令人满意的结果.     

A fast and efficient hybrid fractal-wavelet image coder.

The excellent visual quality and compression rate of fractal image coding have limited applications due to exhaustive inherent encoding time. This paper presents a new fast and efficient image coder that applies the speed of the wavelet transform to the image quality of the fractal compression. Fast fractal encoding using Fisher's domain classification is applied to the lowpass subband of wavelet transformed image and a modified set partitioning in hierarchical trees (SPIHT) coding, on the remaining coefficients. Furthermore, image details and wavelet progressive transmission characteristics are maintained, no blocking effects from fractal techniques are introduced, and the encoding fidelity problem common in fractal-wavelet hybrid coders is solved. The proposed scheme promotes an average of 94% reduction in encoding-decoding time comparing to the pure accelerated Fractal coding results. The simulations also compare the results to the SPIHT wavelet coding. In both cases, the new scheme improves the subjective quality of pictures for high-medium-low bitrates.     

Image analysis by Gaussian-Hermite moments

Orthogonal moments are powerful tools in pattern recognition and image processing applications. In this paper, the Gaussian-Hermite moments based on a set of orthonormal weighted Hermite polynomials are extensively studied. The rotation and translation invariants of Gaussian-Hermite moments are derived algebraically. It is proved that the construction forms of geometric moment invariants are valid for building the Gaussian-Hermite moment invariants. The paper also discusses the computational aspects of Gaussian-Hermite moment, including the recurrence relation and symmetrical property. Just as the other orthogonal moments, an image can be easily reconstructed from its Gaussian-Hermite moments thanks to the orthogonality of the basis functions. Some reconstruction tests with binary and gray-level images (without and with noise) were performed and the obtained results show that the reconstruction quality from Gaussian-Hermite moments is better than that from known Legendre, discrete Tchebichef and Krawtchouk moments. This means Gaussian-Hermite moment has higher image representation ability. The peculiarity of image reconstruction algorithm from Gaussian-Hermite moments is also discussed in the paper. The paper offers an example of classification using Gaussian-Hermite moment invariants as pattern feature and the result demonstrates that Gaussian-Hermite moment invariants perform significantly better than Hu's moment invariants under both noise-free and noisy conditions.     

CONSIDERING NEIGHBORHOOD INFORMATION IN IMAGE FUZZY CLUSTERING

Fuzzy C-means clustering algorithm is a classical non-supervised classification method. For image classification, fuzzy C-means clustering algorithm makes decisions on a pixel-by-pixel basis and does not take advantage of spatial information, regardless of the pixels' correlation. In this letter, a novel fuzzy C-means clustering algorithm is introduced, which is based on image's neighborhood system. During classification procedure, the novel algorithm regards all pixels' fuzzy membership as a random field. The neighboring pixels' fuzzy membership information is used for the algorithm's iteration procedure. As a result, the algorithm gives a more smooth classification result and cuts down the computation time.