一种改进的分形图像压缩算法

为了提高分形图像压缩编码的速度,针对在基本分形图像压缩算法中值域块编码匹配搜索时需要对变换后的定义域块一一对应,导致编码时间较长的缺点,提出了一种基于菱形搜索算法的分形图像压缩编码新算法.菱形搜索算法是一种运动估计的快速搜索算法,主要过程是在所有的候选块中搜索当前块的最优匹配块.通过运用菱形搜索算法中的大小菱形模板进行匹配搜索,实验证明文中算法在提高编码速度和降低编码复杂度是有效的.     

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.     

一种改进的分形图像压缩算法

为了缩短分形编码时间,通过对图像定义域块和值域块的统计特性分析,提出了改进的分形压缩算法。设计最优匹配定义域块自适应搜索方法,缩短搜索范围;用值域块均值代替灰度偏移量,减少计算量。实验证明,运用这种改进的分形图像压缩算法进行图像压缩,在保持较高的解压图像质量的情况下,大幅缩短图像压缩编码时间。     

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     

空形编码技术在超宽带通信系统中的应用

提出一种基于正交Hermite脉冲的超宽带通信系统方案.该方案将波形域分集概念引入到多天线超宽带系统中,拓展了常规空时编码的概念,有效提高了短距离、高速率无线通信的性能;采用合适的脉冲波型,在不同天线之间构成渡形正交.代替常规的编码正交,简化了发射机和接收机的复杂度.仿真结果表明,该方案性能良好,不仅适用于块衰落信道,同样适用于快衰落信道.     

基于小波与分形相结合的图像压缩优化算法

提出了一种基于小波与分形相结合的图像压缩优化算法.首先将图像进行小波分解后,对低频子带单独编码;在分形编码时,根据不同方向子图的纹理特征不同,采用不同形状的块进行分形预测;同时对高频子带使用二次匹配编码,并且匹配运算只在同一方向上进行.实验证明,这种优化算法能够大大缩短分形编码的时间,并取得较高的压缩比.     

时间色散信道中格形编码的正交频分复用(TC-OFDM)技术

本文提出了一种格形编码的正交频分复用（ＴＣ－ＯＦＤＭ）方案，研究把正交频分复用（ＯＦＤＭ）和格形编码（ＴＣＭ）有机地结合，来克服时间色散信道中的频率选择性和多径衰落。文中对码率２／３格形编码的８ＤＰＳＫ－ＯＦＤＭ在时间色散信道中的误码率性能进行了分析和模拟，并讨论了不同正交载波数情况下格形编码的设计。结果表明，在比特误码率ＢＥＲ为１０－３时采用ＴＣ－ＯＦＤＭ方案与未编码系统相比可以提供６ｄＢ的编码增益。     

一种适用于H.263的运动估计搜索算法

本文提出了一种用于视频编码快速运动估计的基于中心三步法（ＣＴＳＳ）的块匹配搜索算法。结果表明,这种算法与Ｈ．２６３的可选项结合使用时,大大减少了块匹配的计算量,并可获得与全搜索算法相当的图像质量、信噪比和编码比特数。     

限制D块耦合极点插值的分形图像压缩算法

由于当前分形图像搜索编码都忽略了对比度因子约束,且对于负载性较高的图像,其压缩比较低,继而降低了解码图像质量以及计算效率。对此,构造了限制D块搜索范围耦合极点插值复原技术的分形图像压缩算法。引入对比度因子约束,设计了一种限制D块搜索范围的编码机制。并基于皮亚诺扫描,提出了极点插值复原技术。测试算法性能,结果显示：与当前的分形图像压缩算法相比,文中算法的压缩效果优异,其平均PSNR提高了ldB～3dB,且编码速度提高了约11．6倍,可满足实时性,在压缩比越高时,提高越明显。     

基于深度学习的分形图像压缩编码模型设计

针对传统编码模型存在的图像分割处理效果不佳、分割精度不高、耗时较长以及编码质量较差的问题，提出基于深度学习的分形图像压缩编码模型。建立图像分割约束条件，对图像进行分割处理，以处理后的图像作为深度学习中ResNet网络模型的输入，提取原始分形图像的特征并将图像块分类；建立特征图像块匹配规则，排序图像块，记录分形码，将分形码作为图像在度量空间内的表现形式，通过度量空间的压缩变换实现分形图像压缩编码。实验结果表明：所提模型的图像压缩编码质量较高，具有一定的应用价值。     

基于DCT变换的快速分形编码方法

本文提出了一种基于DCT变换加速相似块匹配的分形编码方法.通过图像块与相似块在DCT变换域少数低频系数的比较,来确定匹配的相似块.并根据DCT变换的性质,方便的确定对比度因子和亮度偏移量.并采用将图像块与相似块的DCT变换系数预制成查找表的方法减少重复运算.结果表明,编码时间大幅度减少,而PSNR只是略有下降.     

Fast fractal image block coding based on local variances

In fractal image block coding, most of the time is spent on finding a close match between a range block and a large pool of domain blocks. For a large image, this effect becomes aggravated as the domain pool increases exponentially. We propose using the local variances of domain blocks to reduce the search space. By sorting the contracted domain pool according to their local variances and defining an acceptance criterion for a close match, we can confine all the potential close matches to a relatively small sized window to limit the search space. The encoding time can hence be shortened with the decoded image quality as good as that using the full search method. The speedup can be over ten times depending on the complexity of encoded images.     

Still image coding based on vector quantization and fractalapproximation

In this paper, we propose a coding algorithm for still images using vector quantization (VQ) and fractal approximation, in which low-frequency components of an input image are approximated by VQ, and its residual is coded by fractal mapping. The conventional fractal coding algorithms indirectly used the gray patterns of an original image with contraction mapping, whereas the proposed fractal coding method employs an approximated and then decimated image as a domain pool and uses its gray patterns. Thus, the proposed algorithm utilizes fractal approximation without the constraint of contraction mapping. For approximation of an original image, we employ the discrete cosine transform (DCT) rather than conventional polynomial-based transforms. In addition, for variable blocksize segmentation, we use the fractal dimension of a block that represents the roughness of the gray surface of a region. Computer simulations with several test images show that the proposed method shows better performance than the conventional fractal coding methods for encoding still pictures.     

Gradient match and side match fractal vector quantizers for images

In this paper, we propose gradient match fractal vector quantizers (GMFVQs) and side match fractal vector quantizers (SMFVQs), which are two classes of finite state fractal vector quantizers (FSFVQs), for the image coding framework. In our previous work, we proposed the noniterative fractal block coding (FBC) technique to improve the decoding speed and the coding performance for conventional FBC techniques. To reduce the number of bits for denoting the fractal code of the range block, the concepts of the gradient match vector quantizers (GMVQs) and the side match vector quantizers (SMVQs) are employed to the noniterative FBC technique. Unlike ordinary vector quantizers, the super codebooks in the proposed GMFVQs and SMFVQs are generated from the affine-transformed domain blocks in the noniterative FBC technique. The codewords in the state codebook are dynamically extracted from the super codebook with the side-match and gradient-match criteria. The redundancy in the affine-transformed domain blocks is greatly reduced and the compression ratio can be significantly increased. Our simulation results show that 15%-20% of the bit rates in the noniterative FBC technique are saved by using the proposed GMFVQs.     

Iteration-free fractal image coding based on efficient domain pooldesign

The domain pool design is one of the dominant issues which affect the coding performance of fractal image compression. In this paper, we employ the LBG algorithm and propose a block averaging method to design the efficient domain pools based on a proposed iteration-free fractal image codec. The redundancies between the generated domain blocks are reduced by the proposed methods. Therefore, we can obtain the domain pools that are more efficient than those in the conventional fractal coding schemes and thus the coding performance is improved. On the other hand, the iteration process in the conventional fractal coding scheme not only requires a large size of memory and a high computation complexity but also prolongs the decoding process. The proposed iteration-free fractal codec can overcome the problems above. In computer simulation, both the LBG-based and block-averaging methods for the domain pool design in the proposed iteration free scheme achieve excellent performances. For example, based on the proposed block-averaging method, the decoded Lena image has at least a 0.5 dB higher PSNR (under the same bit rate) and an eight-time faster decoding speed than the conventional fractal coding schemes that require iterations.     

一种快速分形图像编码算法

目前,分形图像编码技术最主要的缺点仍然是编码时间太长.因此,如何提高分形图像编码速度成为当前分形图像编码技术的研究热点.本文从分形图像编码通用公式推导出一个不等式,利用此不等式,可以预先排除大量不可能与值域块匹配的定义域块,从而减少值域块与定义域块的匹配计算,以此达到缩短编码时间的目的.实验结果表明,在解码图像质量基本不变情况下,本文的方法所使用的编码时间比Fisher方案所需的编码时间减少了很多.     

一种新的分形图象压缩编码方法

本文首先研究了一般分形图象压缩方法存在的缺陷,然后对它进行了改进.最后在DCT域进行进一步改进。实验结果表明,与原来方法相比较,改进后的方法具有明显的优越性。     

A NEW FRACTAL IMAGE CODING METHOD

Some shortcomings of common fractal image coding methods are studied , then they are corrected with a new method. The new method is improved further in DCT domain. Coding results show the advantage of the new method.     

DCT域网格编码量化及其在图像量化中的应用

本文提出了在离散余弦变换(DCT)域内作网格编码量化(TCQ)的新方法.该方法不仅利用了信号间的时间相关性,而且也利用了信号变换域内的相关性,该方法首先用一维或二维DCT变换减小变换域内的相关性,然后利用卷集编码和信号空间扩展来增大量化信号间的欧氏距离,并用维持比算法寻找最优量化序列.仿真结果表明,基于二维DCT的TCQ方法在相对大的灰度率下,比传统的TCQ方法好2dB左右,与此同时,TCQ又比最优标量量化好2dB左右.该方法还具有编码计算复杂度适中,解码简单以及对误差扩散不敏感的优点.