基因表达式编程在分形图像压缩编码中的应用

针对目前存在图像压缩率不高、全局搜索或遗传算法寻找最优分形图像压缩编码速度慢的不足,将基因表达式编程应用于分形图像压缩编码,提高求解分形图像压缩编码速度和压缩比.首先从理论上对二值图像压缩编码的求解过程和基因表达式编程在压缩编码中的作用机理进行分析;然后,研究分形图像压缩编码的基因和染色体的表示方法,适应度函数设计以及选择、变异、插串、基因变换、基因重组等基因遗传进化操作过程.提出基因表达式编程的分形图像压缩算法,求解分形图像压缩编码的最优解.实验结果表明,基因表达式编程应用于分形图像压缩编码,具有较强的全局寻优能力,搜索最优解的速度比遗传算法快约2倍,图像压缩率高.     

Speed-up fractal image compression with a fuzzy classifier

This paper presents a fractal image compression scheme incorporated with a fuzzy classifier that is optimized by a genetic algorithm. The fractal image compression scheme requires to find matching range blocks to domain blocks from all the possible division of an image into subblocks. With suitable classification of the subblocks by a fuzzy classifier we can reduce the search time for this matching process so as to speedup the encoding process in the scheme. Implementation results show that by introducing three image classes and using fuzzy classifier optimized by a genetic algorithm the encoding process can be speedup by about 40% of an unclassified encoding system.     

A graph-theoretic approach for studying the convergence of fractalencoding algorithm

We present a graph-theoretic interpretation of convergence of fractal encoding based on partial iterated function system (PIFS). First we have considered a special circumstance, where no spatial contraction has been allowed in the encoding process. The concept leads to the development of a linear time fast decoding algorithm from the compressed image. This concept is extended for the general scheme of fractal compression allowing spatial contraction (on averaging) from larger domains to smaller ranges. A linear time fast decoding algorithm is also proposed in this situation, which produces a decoded image very close to the result obtained by an ordinary iterative decompression algorithm.     

基于平方加权质心特征的快速分形图像压缩编码

分形图像压缩利用自身图像具有的相似性,结合压缩仿射变换减少图像数据的冗余来实现图像数据的压缩,具有压缩比高、恢复简单的特点。然而,分形图像压缩编码也具有编码时间长、计算复杂的缺点。为了解决上述的缺点,提出了基于平方加权质心特征的快速分形图像压缩编码算法,利用平方加权质心特征可以将基本分形图像压缩编码过程中的全局搜索转化为局部搜索,限定搜索范围,减少码本数量,在巨大图像信息量传输和存储过程中,在一定程度上缩短了编码时间。将平方加权质心特征快速分形图像压缩编码算法和双交叉和算法、改进叉迹算法、规范五点和算法进行比较,仿真结果表明,所提算法在恢复质量可接受情况下,编码时间具有巨大优势。     

A parallel decoding algorithm for IFS codes without transientbehavior

Iterated function systems (IFSs) have received great attention in encoding and decoding fractal images. Barnsley (1988) has shown that IFSs for image compression can achieve a very high compression ratio for a single image. However, the major drawback of such a technique is the large computation load required to both encode and decode a fractal image. We provide a novel algorithm to decode IFS codes. The main features of this algorithm are that it is very suitable for parallel implementation and has no transient behavior. Also, from the decoding process of this method we can understand the encoding procedure explicitly. One example is illustrated to demonstrate the quality of its performance.     

四线和特征的快速分形图像编码

分形图像编码具有压缩比高、解码速度快、重构图像质量高等特点,但因这种算法在编码时定义域的搜索量十分巨大,导致其计算复杂度高、编码时间过长,阻碍了它的实用性和普遍应用.为解决此问题,文中提出一种基于四线和特征值编码算法,该算法根据匹配均方根误差与四线和特征间的关系,将全局搜索转化为局部搜索(近邻搜索),限定搜索空间,减少定义域块的搜索,从而提高编码速度.仿真实验结果表明:该算法解码图像质量在客观上优于1 -范数特征算法;与基本分形编码算法相比,基于四线和特征算法在主观上不改变重构图像质量,但在编码速度上却得到极大提高.     

基于分形和小波变换的自适应混合图像编码

待编码图像经过金字塔型离散小波变换后的系数在小波域内可以组成分层树状数据结构一个小波树，这些跨越不同分辨率的小波树之间存在一定的相似性，可以通过分形变换来描述，本文正是构造小波树的基础邮基于分形和小波变换的自适应混合图像压缩算法，实验证明，我们提出的图像压缩方法与ＪＰＥＧ相比，能够在相近的压缩比的情况下（６０：１～７０：１）使得重建图像的ＰＳＮＲ（〉２９．５ｄＢ）增加约５．４ｄＢ并且图像的主观视觉     

快速分形图象压缩算法的研究

分形编码是一种非常有潜力的图象压缩技术，但因其与具很高的时间复杂度，故至今未能获得广泛的应用，本文提出了旨在降低分形编码复杂度，缩短编码时间的分形图像压缩改进算法，该算法采取递归四树分块结构，将多种块分类技术相结合，并通过预计算，旋转与翻转标准化等方法降低计算复杂度，采取高效的存储方案来提高压缩化，力求在图像质量，压缩比和编码时间上取得了良好的折Ｚｏｎｇ使分形编码更加实用化。实验结果表明，采用这种     

Study on Huber Fractal Image Compression

In this paper, a new similarity measure for fractal image compression (FIC) is introduced. In the proposed Huber fractal image compression (HFIC), the linear Huber regression technique from robust statistics is embedded into the encoding procedure of the fractal image compression. When the original image is corrupted by noises, we argue that the fractal image compression scheme should be insensitive to those noises presented in the corrupted image. This leads to a new concept of robust fractal image compression. The proposed HFIC is one of our attempts toward the design of robust fractal image compression. The main disadvantage of HFIC is the high computational cost. To overcome this drawback, particle swarm optimization (PSO) technique is utilized to reduce the searching time. Simulation results show that the proposed HFIC is robust against outliers in the image. Also, the PSO method can effectively reduce the encoding time while retaining the quality of the retrieved image.     

基于遗传算法和迭代函数系统的图像压缩方法

分形图像编码是一种具有诱人前景的压缩编码方法,它能够实现很高的压缩比而且主观质量良好。由于分形编码中搜索与给定值域块了相匹配的定义域块的过程耗时很长,因此分形编码速度很低。遗传算法是一种在最优搜索应用中十分常见的高效搜索算法。文中在讨论研究遗传算法效率的基础上,将遗传算法应用到分形图像压缩编码中,有效地解决了分形图像编码中的最优匹配问题,实验结果表明将遗传算法与分形压缩编码有机地结合起来可以大大提     

分散图象压缩的遗传算法

基于分形和PIFS函数的特点，提出一种改进的遗传算法用于分形压缩分块的定义域匹配搜索算法。该方法克服了原来分形图象压缩中分类匹配算法固有的局部最优性和随机搜索的缺点，是一种具导引的全局优化的搜索算法。分析和实验表明，该方法的图象压缩比高，解码质量好，若应用遗传算法的并行性，其编码速度也得到提高。     

Fractal image compression based on Delaunay triangulation andvector quantization

Presents a new scheme for fractal image compression based on adaptive Delaunay triangulation. Such a partition is computed on an initial set of points obtained with a split and merge algorithm in a grey level dependent way. The triangulation is thus fully flexible and returns a limited number of blocks allowing good compression ratios. Moreover, a second original approach is the integration of a classification step based on a modified version of the Lloyd algorithm (vector quantization) in order to reduce the encoding complexity. The vector quantization algorithm is implemented on pixel histograms directly generated from the triangulation. The aim is to reduce the number of comparisons between the two sets of blocks involved in fractal image compression by keeping only the best representative triangles in the domain blocks set. Quality coding results are achieved at rates between 0.25-0.5 b/pixel depending on the nature of the original image and on the number of triangles retained.     

基于混合编码的分形图像压缩方法研究

提出了一种基于混合编码的分形图像压缩方案,改进了分形编码与SP IHT算法,对提升小波变换后的最低频部分采用改进的分形编码,其他部分采用改进的SP IHT算法。试验结果表明,该方法在缩短了图像压缩时间的同时,明显减少了分形压缩恢复图像的方块效应。     

Fractal coding of subbands with an oriented partition

We propose a new image compression scheme based on fractal coding of the coefficients of a wavelet transform, in order to take into account the self-similarity observed in each subband. The original image is first decomposed into subbands containing information in different spatial directions and at different scales, using an orthogonal wavelet-generated filter bank. Subbands are encoded using local iterated function systems (LIFS), with range and domain blocks presenting horizontal or vertical directionalities. Their sizes are defined according to the correlation lengths and resolution of each subband. The edge degradation and the blocking effects encountered at low bit-rates using conventional LIFS algorithm are reduced with this approach. The computation complexity is also greatly decreased by a 12:1 factor in comparison to fractal coding of the full resolution image. The proposed method is applied to standard test images. The comparison with other fractal coding approaches and with JPEG shows an important increase in terms of PPSNR/bit-rate. Especially for images presenting a privileged directionality, the use of adaptive partitions results in about 3 dB improvement in PPSNR. We also discuss the distorsion versus rate improvement obtained on high-frequency subbands when fractal coding instead of pyramidal vector quantization is used. Our approach achieves a real gain in PPSNR for low bit-rates between 0.3 and 1.2 bpp.     

一种新的小波分形变换图像编码算法

提出了一种基于平滑双正交小波和自适应分割算法的小波域分形图像编码算法，在基于离散有限方差(DFV)最优准则下得到了适合图像编码的一种新的平滑双正交小波，从而改善了分块效应。在小波域的分形编码中，提出了一种基于图像信息分布特征的自适应分割算法，实验表明，该文算法在相同压缩比的情况下，解码图像的主观视觉质量和峰值信噪比都明显优于SQS方法、基本分形图像编码方法和SPIHT方法。     

DSC的超光谱图像无损压缩算法

有效的星载超光谱图像压缩技术对于解决超光谱图像实时传输极为重要。针对超光谱图像传统的联合编解码算法的不足,提出了一种基于分布式信源编码(Distributed Source Coding,DSC)的超光谱图像无损压缩算法。为利用超光谱图像的局部空间相关性,将超光谱图像进行分块处理;引入多元线性回归模型构建编码块的边信息,并为每个编码块选取最优的预测阶数,以有效利用超光谱图像的局部谱间相关性。根据(n,k)线性分组码的原理,通过多元陪集码实现超光谱图像的分布式无损压缩。实验结果表明:该算法能够取得较好的无损压缩性能,同时具有较低的编码复杂度,适合星载超光谱图像的压缩实现。     

一种基于方差的匹配方法

文章就分形图象压缩中搜索与匹配过程的相似性提出了一个经过不同对比度实现快速分形编码算法，基于序列块和主块之间当前最小象差的方差信息，该算法排除不必搜索的主块，这样大大减少了每个序列块进行搜索和匹配的主块数与相应的编码时间，该算法在减少的时间内生成了与常规满搜索近乎一致的分形编码。     

A fast encoding algorithm for fractal image compression using theDCT inner product

In this paper, a fast encoding algorithm is developed for fractal image compression. At each search entry in the domain pool, the mean square error (MSE) calculations of the given range block and the eight dihedral symmetries of the domain block are obtained simultaneously in the frequency domain, in which the redundant computations are all eliminated in the new encoding algorithm. It is shown in software simulation that the encoding time is about six times faster than that of the baseline method with almost the same PSNR for the retrieved image. The fast algorithm is performed to deal with the eight dihedral symmetries at each search entry. Therefore, it can be applied to various enhanced algorithms which are equipped with quadtree, classification, and other mechanisms.     

Parameter analysis of fractal image compression and its applications in image sharpening and smoothing

In recent years, numerous fractal image compression (FIC) schemes and their applications in image processing have been proposed. However, traditional FIC ignores the importance of affine parameters in the iterated function system (IFS) and affine parameters are kept invariant for a certain image in almost all of these schemes. By analyzing fractal compression technology in this paper, we show that the affine parameters in IFS can vary with different image quality measurements. A positive correlation exists between the image contrast of fractal decoded image and affine scalar multiplier. This strong correlation demonstrates that an image can be sharpened or smoothed using fractal compression technology.     

Technique for fractal image compression using genetic algorithm

A new method for fractal image compression is proposed using genetic algorithm (GA) with an elitist model. The self transformation property of images is assumed and exploited in the fractal image compression technique. The technique described utilizes the GA, which greatly decreases the search space for finding the self similarities in the given image. This article presents theory, implementation, and an analytical study of the proposed method along with a simple classification scheme. A comparison with other fractal-based image compression methods is also reported.