基于改进小波的图像压缩算法设计与实现

为了减小图像编码中的空间融合,需要进行图像压缩编码。针对当前的LBG图像向量量化压缩算法自适应能力不强的问题,提出一种基于改进小波结合LBG向量量化的图像压缩算法。首先采用双正交小波对原始图像进行正交性分解,采用重构滤波器进行图像的降噪重构,然后运用LBG向量量化方法构造图像的矢量码书,采用三级小波尺度分解进行不同码书尺寸下的图像压缩。最后进行仿真测试,结果表明采用该方法进行图像压缩的信噪比及峰值信噪比较高,说明图像压缩的质量较好,且计算复杂度较低。     

一种新的小波变换零树图象编码

本文在对图象的小波变换进行讨论的基础上，充分利用了小波变换后各子图象的系数对重构图象的重要程度和人眼的视觉特性，对“嵌入式零树编码”进行改进，提出了一种更加实用的图象压缩方案。该方案可获得具有较高压缩比、信噪比和较好视觉效果的恢复图象。     

指纹图象小波压缩中子图信息的研究

图象小波分解后，各子图信息对图象恢复的质量有很大的影响。     

一种改进的嵌入式零树小波编码算法

基于小波变换的静止图象压缩算法是研究视频图象压缩算法的基础和前提.目前基于小波变换的图象压缩算法已出现很多,文章重点研究了嵌入式零树小波编码算法EZW,并通过实验对算法进行了论证.虽然EZW算法能够达到比较好的图象压缩质量及重构图象质量,但是其运算量却相当大,因此文章根据EZW算法存在的问题,对算法进行了改进,最后通过实验对比可知,经本文算法压缩的图象的PSNR明显优于原有算法.     

基于小波变换的盲图象恢复

提出一种基于小波分解的盲图象恢复算法。该算法根据图象小波变换各个子频段所具有的不同频率特性和不同的方向特性，对各子频段分别进行盲图象恢复；各子频段采用不同的正则化算子。实验结果表明该算法的盲图象恢复的性能优于空域算法。     

指纹图象小波压缩中子图信息的研究

图象小波分解后,各子图信息对图象恢复的质量有很大的影响。本文结合指纹图象的特点,明确了各个子图在图象恢复中的重要性,并提出了一种适用于纹理图象的压缩方法,在保证较好的图象恢复质量和较大压缩比的情况下,简化了算法,加快了处理速度。     

基于小波变换的多方向分形预测图象编码方法

小波图象编码属于子带编码,但是,与通常的子带编码不同,图象经过小波变换后,不但分解成不同频带（即分辨率）的子带图象,而且,同分辨率的子带图象还进一步分解成不同空间方向的子带图象。同方向的各个子带图象之间具有相似性,利用这种相似性进行图象编码可以提高压缩比。不同方向的各个子带图象之间虽然不具有相似性,但是,原图象的特征在这些子带图象中的空间位置具有不变性,利用这种空间位置的不变性进行图象编码,可以进一步提高压缩比。本文提供了这方面的理论分析和实例验证。     

基于小波变换的单帧干涉图象分析

本文提出了利用复数小波变换对干涉图象相位进行分块线性拟合的一种新颖的单帧干涉图象分析方法。首先对干涉图象进行适当的分块,然后通过线性相位模型对每个子块的相位进行拟合;拟合系数通过干涉图象分块的小波变换与最小均方误差估计方法获得。该方法克服了傅立叶分析方法的相位去包裹过程以及迭代算法计算量大的缺点。仿真实验表明该方法具有较高的精度和可行性。     

一种改进的分块编码方法研究

本文提出一种分块编码的改进方法,其基本原理是在图象分块编码的基础上,将图象分辨力码用模块子图象作最佳逼近,该方法在较好的图象主客观质量下,获得了较高的图象编码压缩比,且运算量较小,并能较容易在分块编码的硬件基础上将此方法硬化,文中给出了这一改进方法的设计原理以及计算机模拟实结验果。     

图像多小波域矢量量化策略

提出一种基于多小波变换结合矢量量化的图像编码算法(MDWT VQ)。首先对图像进行多小波分解,然后对高频系数用改进后的LBG算法形成的码书进行VQ编码。算法充分利用了多小波域不同分辨率层间各方向子图像的相似性,仅对最高分辨率层进行码书地址索引,低级分辨率层的系数按照一定的组织形式直接套用最高分辨率层的地址索引信息。对比实验的结果验证了该算法在提高图像的重建质量以及在降低位码率方面均比传统的单小波图像编码算法有一定的提高。     

基于小波分析的静止图像分层编码方法

本文提出了一种基于小波分析图像分层编码方法，首先对图像进行小波分解，然后对各子图像进行分类矢量量化（ＣＶＱ），利用不同分辨率级子图像之间的相似性自生成分类信息，模拟实验表明此算法在较好图像质量的情况下获得了高压缩比，和现有文献给出的结果进行比较，该算法具有较好的性能。     

Image coding using wavelet transform and classified vectorquantisation

The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and classified vector quantisation is presented. The input image is first decomposed into a hierarchy of three layers containing ten subimages by the discrete wavelet transform. The lowest resolution low frequency subimage is scalar quantised with 8 bits/pixel. The high frequency subimages are compressed by classified vector quantisation to utilise the crosscorrelation among different resolutions while reducing the edge distortion and computational complexity. Vectors are constructed by combining the corresponding wavelet coefficients of different resolutions in the same orientation and classified according to the magnitude and the position of wavelet transform coefficients. Simulation results show that the proposed scheme has a better performance than those utilising current scalar or vector quantisation schemes     

一种基于小波变换和矢量量化的图像压缩算法

小波变换和矢量量化都是图像压缩中的重要方法。利用小波变换的系数特点,对图像进行小渡分解,对于能量最为集中的低频分量采用标量量化处理,然后将标量量化过程中产生的残差和高频分量一起构造矢量,进行矢量量化。实验结果表明,此算法能够有效提高重构图像质量,获得较高的信噪比。     

An Efficient Image Representation Technique Using Vector Quantization in Multiple Transform Domains

A novel technique for the efficient representation of still images is presented which employs vector quantization in multiple transform domains of the image signal. The system projects the subimages, obtained by partitioning the images used for training, into multiple transform domains. Energy-based split vector quantization is used to form code books in each of these domains. An adaptive algorithm to further optimize the accuracy of the code books in each transform domain is also developed. Simulations using sample imges show that this scheme provides improved reconstruction quality over existing methods for the same compression ratios, or equivalently, employing the proposed technique, fewer bits per pixel are used for the same reconstruction quality. This is achieved at the expense of increased computation at the encoder. The benefits from the improved representation efficiency of ten outweigh the increased computational complexity.     

Image compression using wavelet transform and multiresolutiondecomposition

Schemes for image compression of black-and-white images based on the wavelet transform are presented. The multiresolution nature of the discrete wavelet transform is proven as a powerful tool to represent images decomposed along the vertical and horizontal directions using the pyramidal multiresolution scheme. The wavelet transform decomposes the image into a set of subimages called shapes with different resolutions corresponding to different frequency bands. Hence, different allocations are tested, assuming that details at high resolution and diagonal directions are less visible to the human eye. The resultant coefficients are vector quantized (VQ) using the LGB algorithm. By using an error correction method that approximates the reconstructed coefficients quantization error, we minimize distortion for a given compression rate at low computational cost. Several compression techniques are tested. In the first experiment, several 512x512 images are trained together and common table codes created. Using these tables, the training sequence black-and-white images achieve a compression ratio of 60-65 and a PSNR of 30-33. To investigate the compression on images not part of the training set, many 480x480 images of uncalibrated faces are trained together and yield global tables code. Images of faces outside the training set are compressed and reconstructed using the resulting tables. The compression ratio is 40; PSNRs are 30-36. Images from the training set have similar compression values and quality. Finally, another compression method based on the end vector bit allocation is examined.     

基于小波理论的矢量量化遥感图像压缩方法

分析了遥感图像的统计特性，提出了适合遥感图像压缩的矢量量化与小波变换相结合的压缩方法。该方法将遥感图像小波变换后高频子图划分为一定大小的的像块，对局部相关性较强、灰度变化较小的像块进行高倍压缩；对局部相关性较小、灰度变化较大的像块进行高保真压缩。实验表明，本方法具有良好的压缩性能，适用于遥感图像的压缩。     

一种基于小波包树的图像压缩方法

在过去十年中对图像压缩的研究呈持续增长趋势,在这个领域里最有效和最具代表性的方法是离散小波变换。图像的压缩包括变换、量化和编码。提出了图像的变换和量化方案。该算法采用小波包实现变换,在香农熵的基础上重建最佳树,并且为了量化采用了自适应阈值。相对小波变换的压缩,提供了一种很好的压缩实现。最后实验结果显示了该算法的优越性。     

基于自适应小波变换的嵌入图像压缩算法

针对遥感、指纹、地震资料等图像纹理复杂丰富、局部相关性较弱等特点,文章通过实施自适应小波变换、合理确定系数扫描次序、分类量化小波系数等措施,提出了一种高效的图像压缩编码算法.仿真结果表明,相同压缩比下,本文算法的图像复原质量明显优于SPIHT算法(特别是对于纹理图像,如标准图像Barbara).     

A novel fingerprint image compression technique using wavelets packets and pyramid lattice vector quantization

A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantization , a new vector quantization scheme based on an accurate model for the distribution of the wavelet coefficients is presented. The model is based on the generalized Gaussian distribution. We also discuss a new method for determining the largest radius of the lattice used and its scaling factor , for both uniform and piecewise-uniform pyramidal lattices. The proposed algorithms aim at achieving the best rate-distortion function by adapting to the characteristics of the subimages. In the proposed optimization algorithm, no assumptions about the lattice parameters are made, and no training and multi-quantizing are required. We also show that the wedge region problem encountered with sharply distributed random sources is resolved in the proposed algorithm. The proposed algorithms adapt to variability in input images and to specified bit rates. Compared to other available image compression algorithms, the proposed algorithms result in higher quality reconstructed images for identical bit rates.