一种高效的二维提升小波变换方法

基于对通用处理器L1 Cache命中机制和二维提升小波变换结构的分析,提出一种在Intel通用处理器平台上,采用Streaming SIMD Extensions 2(SSE2)实现高效快速二维提升小波变换的方法.此法对原始小波系数存储层次进行调整,充分利用SSE2的SIMD并行处理能力,尽量保持列变换时数据读取与Cache命中方向一致,有效缩短了二维提升小波变换的执行时间.     

基于小波变换和CSF的图像融合方法

将HVS模型的CSF用于像素层图像融合中是一种新的方法.首先,将源图像进行一层的二维离散小波分解,得到近似系数和细节系数;其次,近似部分采用加权平均方法,细节部分采用CSF滤波方法,来得到融合后的小波系数;最后,将得到的小波系数进行逆小波变换,即得最后的融合图像.此方法计算量小,融合图像符合人类视觉系统,可应用于工程实践中.     

基于小波变换的红外图像滤波

为了提高红外图像的质量,对图像的滤波与增强是必需的,基于小波变换的滤波方法可以在一定程度上将噪声和图像细节分离,从而在基本不损失细节的情况下将噪声滤除.简要介绍了小波、小波变换、小波逆变换、离散二维小波变换和离散二维小波逆变换的基本概念.重点论述了小波变换的实现方法和小波滤波的实现方法.最后通过不同滤波方案的对比说明了基于小波变换的滤波方案的优越性.     

二维离散小波变换的VLSI实现

小波变换图像编码获得了比传统DCT变换编码更好的图像质量和更高的压缩比,然而,实时二维小波变换需要大量运算,因此,专用小波变换芯片的设计已成为小波图像编码中的关键技术,文章提出了一种高速的二维小波变换的VLSI结构。根据模块化的设计思想,设计出一组二维小波变换的基本模块。通过将这些模块按变换要求适当组装,完成了多级二维小波变换,编写了相应的VerilogHDL模型,并进行了仿真和逻辑综合。     

小波变换在电能质量扰动检测中的探讨

文中针对暂态电能质量问题,主要在电能质量扰动检测方面做了一些工作,在小波变换局部模极大值理论的基础上,针对连续小波变换的计算量大,存在较大冗余的缺点,采用二进小波变换对电能质量扰动进行检测。小波变换的局部模极大值对应信号的突变点,可以用来检测电能质量扰动。     

基于小波变换的混沌图像置乱加密算法

给出一种在二维小波变换基础上进行混沌映射,将图像加密的方法。该方法应用二维小波分解算法分解图像信息,再对其实行正弦混沌映射,从而完成图像的加密。解密时,首先对系数进行正弦映射的逆映射,再进行二维小波重构,实现对原始图像的解密。仿真结果证明了该方法的有效性。     

基于提升小波变换的超混沌图像加密方案

研究了一种基于提升小波变换与二维超混沌系统的图像加密方案.首先将图像进行整数提升小波变换,根据自适应排列置乱算法对提升小波变换后的低频子带系数进行调整置乱.然后由两个二维超混沌系统生成混沌序列并进行预处理,同时构造双混合反馈系统.最后利用双混合反馈系统对小波重构后的图像进行像素灰度值替代加密.仿真结果表明,该加密方案弥补了图像经传统小波变换后不能无失真重构的缺陷,并克服了单一的低维混沌系统保密性不高的缺点,在密钥空间和抵抗各种攻击方法上均有较好的改进,具有较高的安全性和实用性.     

基于3维SPIHT编码的超光谱图像压缩

提出一种针对超光谱图像压缩的3维SPIHT编码算法.通过对超光谱图像进行3维小波变换,同时去除像素数据间的空间冗余和谱间冗余.针对变换后得到的小波系数,构造一种3维空间方向树结构,并用经3维扩展后的SPIHT算法(3D SPIHT算法)对小波系数进行量化编码.实验证明,基于3维小波变换的3维SPIHT编码算法在对超光谱图像压缩时,表现出了优良的率失真性能.并且算法复杂度适中,具有嵌入式特性.     

离散小波变换加速导体目标散射计算的分析

如今矩量法被广泛地应用于解决电磁场问题中,但是应用矩量法所得到的矩阵是稠密阵,如果直接求解无疑耗费大量的时间,若能将矩阵稀疏化,那么求解起来就会省时得多.针对这个问题,本文将给出离散小波变换方法稀疏化矩量矩阵,并以Daubechies三阶尺度函数为例,阐述小波的一些基本性质以及滤波系数的构造,论述小波变换原理和详细推导了变换矩阵的构造.并给出一个全新的算例--二维导电平面上的平行双槽线上的散射问题.     

纯二维5/3小波变换及其在CT图像无损压缩中的应用

小波变换在数字图像处理领域有着广泛的应用,其对图像的处理常采用行列分离处理方式,这种方式不能完全吻合人眼视觉特性.针对这一情况,构造了一种与人眼视觉特性更加吻合的纯二维小波变换处理方式.首先,由一维5/3小波滤波器组通过McClellan变换构造纯二维5/3小波滤波器组,并用提升格式实现;然后,用该提升格式与纯二维Lazy小波滤波器组相嵌套的形式实现图像的纯二维5/3小波变换.为了便于工程应用,给出了其变换规程.将纯二维5/3小波变换用于CT图像的无损压缩,实验证明:对于512 dpi×512 dpi尺寸的CT图像,纯二维5/3小波变换无损压缩效果高于二维可分离5/3小波变换,每幅图像可平均节省1 989.9 byte.     

基于小波的感兴趣区域无人机侦察图像的压缩

由于无人机飞行高度高、速度快,所以无人机侦察图像的目标和细节多、数据量大。为了实现图像数据的高速实时传输,需要对无人机侦察图像进行高压缩比压缩;但是在实际应用中,往往只对某些细节和目标感兴趣,要求这些区域清晰可见,这与图像的高压缩比压缩相矛盾,因此,本文提出了一种基于小波的无人机侦察图像压缩方法。该方法将感兴趣的军事目标从原图像中分离开来,对ROI（感兴趣区域）采用低压缩比甚至无损压缩,而对BG（背景区域）采用高压缩比压缩,最后合成两幅图像,解决了无人侦察机图像高压缩比和高质量之间的矛盾。     

提升小波变换与分形结合的图像压缩算法

遥感图像在环境监测、军事侦察等多方面有着广泛应用,然而遥感图像包含信息量大,对其进行压缩来提高存储效率具有重要意义.传统分形编码由于压缩比大的特点被广泛应用到遥感图像压缩中,但是传统分形编码存在压缩时间太长的问题.提出提升小波变换与改进分形结合的压缩方法,把提升小波变换后的低频分量进行基于最小方差搜索法的分形压缩.实验结果表明,提升小波变换与改进的分形结合的压缩方法与小波变换与分形结合的压缩方法相比,在峰值信噪比保持在35 dB不变的情况下,压缩时间大约可以缩短8倍,图像压缩比也有提高.     

Four-dimensional wavelet compression of arbitrarily sized echocardiographic data

Wavelet-based methods have become most popular for the compression of two-dimensional medical images and sequences. The standard implementations consider data sizes that are powers of two. There is also a large body of literature treating issues such as the choice of the "optimal" wavelets and the performance comparison of competing algorithms. With the advent of telemedicine, there is a strong incentive to extend these techniques to higher dimensional data such as dynamic three-dimensional (3-D) echocardiography [four-dimensional (4-D) datasets]. One of the practical difficulties is that the size of this data is often not a multiple of a power of two, which can lead to increased computational complexity and impaired compression power. Our contribution in this paper is to present a genuine 4-D extension of the well-known zerotree algorithm for arbitrarily sized data. The key component of our method is a one-dimensional wavelet algorithm that can handle arbitrarily sized input signals. The method uses a pair of symmetric/antisymmetric wavelets (10/6) together with some appropriate midpoint symmetry boundary conditions that reduce border artifacts. The zerotree structure is also adapted so that it can accommodate noneven data splitting. We have applied our method to the compression of real 3-D dynamic sequences from clinical cardiac ultrasound examinations. Our new algorithm compares very favorably with other more ad hoc adaptations (image extension and tiling) of the standard powers-of-two methods, in terms of both compression performance and computational cost. It is vastly superior to slice-by-slice wavelet encoding. This was seen not only in numerical image quality parameters but also in expert ratings, where significant improvement using the new approach could be documented. Our validation experiments show that one can safely compress 4-D data sets at ratios of 128:1 without compromising the diagnostic value of the images. We also display some more extreme compression results at ratios of 2000:1 where some key diagnostically relevant key features are preserved.     

Performance analysis of image compression using wavelets

The aim of this paper is to examine a set of wavelet functions (wavelets) for implementation in a still image compression system and to highlight the benefit of this transform relating to today's methods. The paper discusses important features of wavelet transform in compression of still images, including the extent to which the quality of image is degraded by the process of wavelet compression and decompression. Image quality is measured objectively, using peak signal-to-noise ratio or picture quality scale, and subjectively, using perceived image quality. The effects of different wavelet functions, image contents and compression ratios are assessed. A comparison with a discrete-cosine-transform-based compression system is given. Our results provide a good reference for application developers to choose a good wavelet compression system for their application     

基于最优小波包基的电能质量扰动数据的压缩

针对电能质量扰动信号的频率变化十分广泛（最高达到MHz）的特点，在小波压缩的基础上，提出将基于最佳小波包基的数据分析方法应用于电能质量数据压缩，利用最佳小波包基的快速搜寻算法对高，低频分量同时进行分解，再对扰动数据进行压缩重构，并将其与基于小波变换的数据压缩方法进行仿真比较。结果表示，对于暂态信号，在压缩比大致相同的情况下，基于最佳小波包基的数据压缩效果比基于传统小波的数据压缩效果好，而对于稳态信号，基于两种方法的压缩效果相当．     

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     

Low bit-rate efficient compression for seismic data

Some marine seismic data sets exceed 10 Tbytes, and there are seismic surveys planned with a volume of around 120 Tbytes. The need to compress these very large seismic data files is imperative. Nevertheless, seismic data are quite different from the typical images used in image processing and multimedia applications. Some of their major differences are the data dynamic range exceeding 100 dB in theory, very often it is data with extensive oscillatory nature, the x and y directions represent different physical meaning, and there is significant amount of coherent noise which is often present in seismic data. Up to now some of the algorithms used for seismic data compression were based on some form of wavelet or local cosine transform, while using a uniform or quasiuniform quantization scheme and they finally employ a Huffman coding scheme. Using this family of compression algorithms we achieve compression results which are acceptable to geophysicists, only at low to moderate compression ratios. For higher compression ratios or higher decibel quality, significant compression artifacts are introduced in the reconstructed images, even with high-dimensional transforms. The objective of this paper is to achieve higher compression ratio, than achieved with the wavelet/uniform quantization/Huffman coding family of compression schemes, with a comparable level of residual noise. The goal is to achieve above 40 dB in the decompressed seismic data sets. Several established compression algorithms are reviewed, and some new compression algorithms are introduced. All of these compression techniques are applied to a good representation of seismic data sets, and their results are documented in this paper. One of the conclusions is that adaptive multiscale local cosine transform with different windows sizes performs well on all the seismic data sets and outperforms the other methods from the SNR point of view. All the described methods cover wide range of different data sets. Each data set will have his own best performed method chosen from this collection. The results were performed on four different seismic data sets. Special emphasis was given to achieve faster processing speed which is another critical issue that is examined in the paper. Some of these algorithms are also suitable for multimedia type compression.     

基于提升小波变换的雷达视频数据实时压缩算法

雷达视频数据压缩具有大数据量、需要高保真度和实时处理的特点.而现有的一些高保真数据压缩算法都比较复杂,不能满足实时处理的要求.已有的硬件实现算法虽然可以实时处理,但其保真度差,同时难以调整压缩比.通过对实际雷达视频回波数据的分析,表明该数据具有相关性强、相关长度短的特点.在此基础之上,本文提出了一种基于提升格式5-3小波变换和简单的Golomb-Rice编码方法的压缩算法,该算法具有低复杂、高保真的特点,并对算法的运算量进行了详细地分析.实测实验表明,通过软件方式实现该算法即可满足雷达视频数据实时压缩的要求.最后,该算法已被成功应用到雷达海情记录系统中.     

基于免疫克隆选择的图像压缩算法研究

针对在图像压缩过程中压缩质量与压缩时间相互间存在矛盾,文中提出了一种基于免疫克隆选择的图像压缩算法。该算法结合免疫克隆算法的抗体的多样性、全局寻优和小波变换多分辨率分析的特点。在压缩过程中不仅采用了小波分解后的低频分量,还加入了一定的高频部分,使压缩后的图像信噪比更低,从而保证了图像的质量。实验结果表明,所提出的图像压缩方法具有相对较高的压缩比和良好的压缩效果。     

深度学习驱动的智能电网运行图像数据压缩技术

随着智能电网的快速发展,用于监视电网运行状况的测量设备大规模投入,其产生的海量运行图像等监视数据由于规模大、维度高、数据冗余等问题难以得到有效利用。为了进一步提高电力大数据的分析应用能力,文中提出一种基于深度学习的电网运行图像数据压缩方法,考虑电网图像监视数据在时序上的耦合关联,通过卷积神经网络对电网运行图像数据进行压缩,有效减少了电网运行图像数据的冗余度。与其他方法相比,基于卷积神经网络的图像数据压缩模型不依赖于人工的数据特征提取和工程经验,可以直接以电网中采集到的原始图像数据的灰度函数作为模型的输入,将数据的特征提取和分类合二为一,实现电网运行图像数据的高效、便捷压缩。通过仿真进行了文中所提方法有效性的验证,结果表明,与其他神经网络相比,所提方法在电网图像压缩效率及压缩精度中具有较强优势。