分形编码在图像检索中的应用

分形编码在图像压缩方面取得了很好的效果,同时,分形编码也能够用于基于内容的图像检索.本文提出了一种基于块限制的分形编码算法和匹配策略,并将它们用于图像检索.在我们编码算法中,图像会被预先分成互相不重叠的子图像块,然后对这些子图像进行独立地分形编码,从而获得整幅图像的分形码.该编码算法能够在很大程度上减少编码时间.在进行图像间相似性的匹配时,我们采用改进的基于九叉树的分配策略,从而避免全局地进行分形码的匹配,减少了计算量.实验结果说明,我们的编码算法和匹配策略能够比较有效地应用于基于内容的图像检索,在计算时间和存储时间上都优于实验中其它两种方法.     

一种基于分形编码的图像分割算法

本文提出了一种新的基于分形编码的图像分割算法,其工作原理和实现方法与现有的基于分形编码的图像分割方法完全不同。新算法利用不同尺度间图像块的自相似性,根据编码的结果为不同像素之间定义生成关系,通过选择合适的误差度量使满足生成关系的各像素灰度值足够接近。同时用生成关系导出相似关系,并把图像分割问题转化为求解像素相似关系的等价类问题。在实现中通过求解相似关系关系图的连通分量来求解相似关系的等价类。实验结果表明本文算法可较有效地分割图像,为基于分形编码的图像分割体系提供了新的方法。     

多级分类分形块编码

文中提出了多级分类分形块编码，在编码时，利用多级四叉树剖分方法，将图像块剖分成不同大小的平坦子块和非平坦子块，分别采用均值编码和分形编码。在对值域块进行分形编码时，为缩短分形编码时间，将值域块和定义域块分成７２类，并分别在同类进行仿射匹配，实验结果显示，多级分类分形块编码极大地缩短了编码时间，同时也提高了分形编码的压缩比。     

基于Hauadorff测度的图像分类器设计

本文采用基于Ｈａｕｓｄｏｒｆｆ测度的分类方法为分类静止图像编码算法设计了一种有效地图像块分类器，根据图像块的办部特征将其分为四大类：平坦区（Ｓｈａｄｅ），中间区（Ｍｉｄｒａｎｇｅ），边缘区（Ｅｄｇｅ），混合区（Ｍｉｘｅｄ），引入分形参数进行的定量分析增强了分类的通用性并改进了分类效果。同时，在一定的假设条件下，根据视觉熵（ＶＥ）的概念和视觉系统（ＨＶＳ）特性从第四类的－Ｍｉａｅｄ块中进一步分离出两     

基于Hausdorff测度的图像分类器设计

本文采用基于Ｈａｕｓｄｏｒｆｆ测度的分类方法为分类静止图像编码算法设计了一种有效的图像块分类器，根据图像块的内部特征将其分为四大类：平坦区（Ｓｈａｄｅ），中间区（Ｍｉｄｒａｎｇｅ），边缘区（Ｅｄｇｅ），混合区（Ｍｉｔｅｄ），引入分形参数进行的定量分析增强了分类的通用性并改进了分类效果．同时，在一定的假设条件下，根据视觉熵（ＶＥ）的概念和人的视觉系统（ＨＶＳ）特性从第四类一Ｍｉｘｅｄ块中进一步分离出两种特殊区域一兴趣焦点（Ｋｅｙ－ｐｏｉｎｔ）和随机纹理区（Ｒａｎｄｏｍ－ｔｅｘｔｕｒｅ），使在主观视觉质量意义上，编码策略的最佳安排和编码系数的合理比特率分配成为可能．     

基于分形的图像修复算法

 图像修复是目前图像处理领域中的一个研究热点,对于较大孔洞的修复一直是个难点问题,已有算法都未能很好地解决.本文基于分形相关理论,提出了一种新的修复算法,很好地利用了图像的整体信息.论述了分形维数和分形编码序列块大小之间的关系,提出多尺度的分形编码及重构的修复方法.为了强化图像细节信息,进行了分形局部迭代.为了提高图像修复的质量,将图像进行了分形放大,再进行分形插值修复.从实验结果可以看出,新方法取得了较好的修补效果,尤其是对纹理图像和有较大孔洞的图像效果更好.     

使用拼贴误差直方图的分形图像检索

拼贴误差是值域块与最匹配的定义域块相似性的一个度量,然而很多基于分形编码参数的分形图像检索技术却忽略了拼贴误差。该文使用拼贴误差直方图作为检索条件,来判定图像的相似度。对有200多张纹理图像的数据库的实验显示,该方法不仅减少了计算的复杂度,而且也有一定的检索准确率。     

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。     

一种混合的四叉树分形图像编码

为克服四叉树分形编码中纹理信息丰富的图像块较难找到满足要求相似块的不足,在四叉树分形图像编码的基础上,提出了一种与BTC方法结合的混合分形编码方法。讨论了四叉树分形编码与BTC方法,综合了2种方法的优点,对四叉树分形编码中块尺寸为4×4且找不到满足要求相似块的图像块,采用BTC方法,并用标准图像进行仿真实验,给出了四叉树编码方法与本文方法的实验结果。实验结果表明:此方法能明显改善重构图像的MSE或PSNR指标。     

图像的方向特性与分形——小波变换编码

本文对一类采用小波变换和ＩＦＳ（Ｉｔｅｒａｔｅｄ Ｆｒａｃｔａｌ Ｓｙｓｔｅｍ）分形编码相结合的图像编码方法的结构进行了深入分析,构造了这类算法的基本数学框架,并通过对标准图像内在相关性的统计分析得出了自然图像具有方向自仿射性的结论。同时还对分别属于水平、垂直和对角方向的尺度压缩因子是相互独立的情况进行编／解码,实验证明对不同方向的尺度压缩因子采用不同的编码可使编码质量有在的提高。     

基于视觉特性的分形图像压缩编码

介绍了分形的基本概念及分形的数学原理，并针对传统的分形图像压缩编码算法的不足之处，提出了一种基于视觉特性的分形压缩算法，该方法利用人眼对灰度变化的敏感程度与背景有关的特点，在综合考虑图像块的均值与方差的前提下，动态地改变分形压编码时定义域块仿射变换后拼贴到值域块的最小误差，从而提高了编码速度。     

基于分形的混合图象压缩方法

分形图象压缩方法是近年来迅速兴起的一种高倍率图象压缩方法，它依据分形原理，利用迭代函数系统（IFS）来抽取自然图象中的自相似性，达到压缩图象的目的；解码时利用拼帖定理来快速恢复图象。然而它最大的缺点是速度太慢。为此，本文将分形图象压缩同传统的块截取变换方法（BlockTruncatingCoding）结合，在压缩速度、压缩倍数和压缩失真方面有个折衷，达到较好的性能。     

图像小波变换的分形编码技术

简要介绍小波变换在图像处理上的基本原理，初步探讨小波变换的分区块编码算法，在小波分解的基础上结合人眼特性，利用子带的相关性，引进分形编码思想，对图像进行有效压缩。仿真实验结果说明，该方法在同等图像的恢复质量下，其压缩比优于传统的压缩方法，在提高图像的恢复质量方面也有一定价值。     

分形与图像压缩编码

本文概述分形图像压缩的理论基础,介绍分形图像编码的IFS方法,讨论小波技术在分形图像编码中的应用。     

Isometry-Based Shape-Adaptive Fractal Coding for Images

Fractal image coding is an effective method to eliminate the image redundancy through piecewise self-transformability. The fractal code consists of a set of contractive affine transforms. To improve the performance when a range block experiences large error, we usually partition the range block into square or nonsquare subrange blocks for two- or multilevel fractal coding. In this paper, we find an inherent property of fractal coding that can be used to decide the edge orientation of a range block. Then this property is used for shape-adaptive fractal coding (SAFC). In SAFC, the top-level range block is partitioned into square or nonsquare (rectangle or triangle) subrange blocks for multilevel fractal encoding. Here, the maximum size of the range block can be the same as that of the whole image size while the minimum size is 4×4. In SAFC, no additional computations are required to obtain the edge orientation of a range block. Instead, we propose an edge-orientation detector, where the edge orientation of a range block is obtained during the fractal encoding process. According to our simulation results, SAFC can reduce the bit rate requirement of the conventional fractal coding scheme.     

Content-based image retrieval using block-constrained fractalcoding and nona-tree decomposition

Fractal coding has been proved useful for image compression. In fractal coding, an image is represented by a number of self-transformations (fractal code) by which an approximation of the original image can be reconstructed. The authors present a block-constrained fractal coding scheme and a nona-tree decomposition based matching strategy for content-based image retrieval. In the coding scheme, an image is partitioned into non-overlapped blocks with a size close to that of a query iconic image. The fractal code is generated for each block independently. In the similarity measure of the fractal code, an improved nona-tree decomposition scheme is adopted to avoid matching the fractal code globally in order to reduce computational complexity. The experimental results show that the authors' coding scheme and matching strategy are useful for image retrieval, and compare favourably with two other methods tested in terms of storage usage and computing time     

机动目标的逆合成孔径雷达成像原理与算法

对于非合作的机动目标,由于目标相对于雷达射线的姿态和转速难以测定,而且是时变的,因而给逆合成孔径雷达(ISAR)成像造成较大困难.本文讨论了这种情况下成像的一般原理,并对机动性不太大,散射点子回波多普勒变化满足一阶近似条件时,提出了实用算法.实测数据的处理结果说明新算法是可行的.     

Image coding based on a fractal theory of iterated contractiveimage transformations

The author proposes an independent and novel approach to image coding, based on a fractal theory of iterated transformations. The main characteristics of this approach are that (i) it relies on the assumption that image redundancy can be efficiently exploited through self-transformability on a block-wise basis, and (ii) it approximates an original image by a fractal image. The author refers to the approach as fractal block coding. The coding-decoding system is based on the construction, for an original image to encode, of a specific image transformation-a fractal code-which, when iterated on any initial image, produces a sequence of images that converges to a fractal approximation of the original. It is shown how to design such a system for the coding of monochrome digital images at rates in the range of 0.5-1.0 b/pixel. The fractal block coder has performance comparable to state-of-the-art vector quantizers.     

Image coding by block prediction of multiresolution subimages

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but it has not been fully recognized and exploited for general images. Fractal block coders have exploited the self-similarity among blocks in images. We devise an image coder in which the causal similarity among blocks of different subbands in a multiresolution decomposition of the image is exploited. In a pyramid subband decomposition, the image is decomposed into a set of subbands that are localized in scale, orientation, and space. The proposed coding scheme consists of predicting blocks in one subimage from blocks in lower resolution subbands with the same orientation. Although our prediction maps are of the same kind of those used in fractal block coders, which are based on an iterative mapping scheme, our coding technique does not impose any contractivity constraint on the block maps. This makes the decoding procedure very simple and allows a direct evaluation of the mean squared error (MSE) between the original and the reconstructed image at coding time. More importantly, we show that the subband pyramid acts as an automatic block classifier, thus making the block search simpler and the block matching more effective. These advantages are confirmed by the experimental results, which show that the performance of our scheme is superior for both visual quality and MSE to that obtainable with standard fractal block coders and also to that of other popular image coders such as JPEG.