采用树结构矢量组合的小波图像分类矢量量化

本文提出了采用树结构矢量组合对小波图像进行分类矢量量化的新方法.该方法充分利用了子带系数的带间和带内的相关性,分类信息占用比特数少,并采用了基于人眼视觉特性的加权均方误差准则进行矢量量化,提高了量化增益.仿真结果表明,该方法实现简单,可达到很好的压缩效果.     

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。     

基于多级零树编码的小波系数网格编码量化

该文提出了对小波图像作多级零树编码后进行网格编码量化（TCQ）的新方法。首先利用子带间的相关性进行零树编码，然后利用卷积编码和信号空间扩展来增大量化信号间的欧氏距离，并用Viterbi算法寻找最优量化序列。仿真结果表明，该方法比零树编码后采用最优量化要提高0．3dB左右。该方法还具有编码计算复杂度适中，解码简单的优点。     

改进的图像多分辨率零树小波编码算法

本文在研究Ｓｈａｐｉｒｏ零树方法进行小波图像编码基础上，提出一种改进型多分辨率零树编码算法。本算法从客观信噪比的角度出发，以码流中增加单位码字平均所能减少的系数量化误差为依据，确定各部分小波系数参加编码的顺序，同等码率下可提高ＰＳＮＲ０．１～０．５ｄＢ，同时改进后的算法可以使编码产生的码流在原来支持多码率解码的基础上，支持多分辨率图像解码     

小波方向子树的分形量化与零树量化相结合

本文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,并首次提出了小波方向子树量化的概念。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采用小波方向子树量化进行了编/解码分析,同时深入研究了零树小波量化与小波方向子树量化之间的相互关系。实验证明,采用小波方向子树量化与零树量化的混合编码算法可使编码质量有较大的改善。     

基于小波零树的图像感知熵编码研究

本文以小波作为编码框架，通过对人工合成图像的主要观实验，得出小波变换域中各不同频带的视觉误差差值函数，进而提出了一个新的量化编码方案。     

基于整数小波变换的嵌入式零树编码算法

在讨论整数小波变换的基础上,分析了嵌入式零树小波算法,针对EZW算法存在的一些问题,提出了一种基于整数小波变换的嵌入式零树编码算法。讨论了该算法考虑的几点因素的改进方法,详细研究了该算法的搜索过程和流程,并利用一个序列进行测试,测试结果表明,EZIWT算法能够提高压缩率,减少时间复杂度,低比特率时更明显。     

采用空间矢量组合的小波图像分类矢量量化

该文提出了采用空间矢量组合对小波图像进行分类矢量量化的新方法。该方法充分利用了各高频子带系数的频率相关性和空间约束性将子带系数重组，依据组合矢量能量和零树矢量综合判定进行分类，整幅图像只需单一量化码书，分类信息占用比特数少，并采用了基于人眼视觉特性的加权均方误差准则进行矢量量化，提高了量化增益。仿真结果表明，该方法实现简单，在较低的编码率下，可达到很好的压缩效果。     

零树框架下整数小波图像编码的改进

整数小波变换(Integer Wavelet Transform)有许多优点，但是图象经整数小波变换（IWT)后，能量集中性较第一代小波变换差很多，不利于嵌入式零树编码(Embedded Zerotree Wavelet Encoding)。因此本文提出一种新算法，从两方面加以改进。首先，采用“整数平方量化阈值选取算法”，根据整数小波变换后各子带系数幅值的动态变化较小，小波图像能量较一般小波差的特点，选取从1开始的正整数平方作为量化闽值的同时引入可调节的量化阈值系统，根据图像中不同区域的重要性选取与之相应的量化阈值，从而增加了零树的数量；其次，提出基于索引表和游程编码的小波零树编码的新思路，简化了编码与解码的过程。实验表明，本文算法充分的将整数小波变换与零树编码结合在一起，改善了压缩质量，提高了压缩效率。     

Smoothness-constrained quantization for wavelet image compression

Uniform quantization of wavelet coefficients introduces perceptually disturbing artifacts by decreasing the visual smoothness. In this paper, the local Holder regularity is used to quantify these changes in visual smoothness. A modified uniform quantization scheme that constrains local regularity reduction is proposed. The quantizer modification is applicable to any uniform quantization scheme and requires no additional side information to be transmitted to the decoder, unlike standard deadzone quantizers. The reconstructed images show a noticeable perceptual improvement as well as an increased PSNR relative to uniform quantization.     

Space-frequency quantization for wavelet image coding

A new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree-structured quantization (related to spatial structures) has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical representation. This paper addresses the problem of how spatial quantization modes and standard scalar quantization can be applied in a jointly optimal fashion in an image coder. We consider zerotree quantization (zeroing out tree-structured sets of wavelet coefficients) and the simplest form of scalar quantization (a single common uniform scalar quantizer applied to all nonzeroed coefficients), and we formalize the problem of optimizing their joint application. We develop an image coding algorithm for solving the resulting optimization problem. Despite the basic form of the two quantizers considered, the resulting algorithm demonstrates coding performance that is competitive, often outperforming the very best coding algorithms in the literature.     

Hierarchical quantization indexing for wavelet and wavelet packet image coding

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.     

Dynamic contrast-based quantization for lossy wavelet image compression.

This paper presents a contrast-based quantization strategy for use in lossy wavelet image compression that attempts to preserve visual quality at any bit rate. Based on the results of recent psychophysical experiments using near-threshold and suprathreshold wavelet subband quantization distortions presented against natural-image backgrounds, subbands are quantized such that the distortions in the reconstructed image exhibit root-mean-squared contrasts selected based on image, subband, and display characteristics and on a measure of total visual distortion so as to preserve the visual system's ability to integrate edge structure across scale space. Within a single, unified framework, the proposed contrast-based strategy yields images which are competitive in visual quality with results from current visually lossless approaches at high bit rates and which demonstrate improved visual quality over current visually lossy approaches at low bit rates. This strategy operates in the context of both nonembedded and embedded quantization, the latter of which yields a highly scalable codestream which attempts to maintain visual quality at all bit rates; a specific application of the proposed algorithm to JPEG-2000 is presented.     

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。     

Haar小波在数字图像水印算法上的探究

近年随着数字化时代的逐步进入到我们的生活中数字图像、音频、视频的应用也越来越广,但同时各种修改、复制和窃取等各种数字信息的安全性问题也凸显了出来。在这样的背景下,本文将利用小波分析来研究作为强有力信息保护手段---数字水印的算法。首先对数字水印进行混合置乱,使其与原水印有较大差别;然后利用Haar小波将数字水印对载体图像进行嵌入和提取,从而达到具有更高安全性的目的。     

Feature-based wavelet shrinkage algorithm for image denoising.

A selective wavelet shrinkage algorithm for digital image denoising is presented. The performance of this method is an improvement upon other methods proposed in the literature and is algorithmically simple for large computational savings. The improved performance and computational speed of the proposed wavelet shrinkage algorithm is presented and experimentally compared with established methods. The denoising method incorporated in the proposed algorithm involves a two-threshold validation process for real-time selection of wavelet coefficients. The two-threshold criteria selects wavelet coefficients based on their absolute value, spatial regularity, and regularity across multiresolution scales. The proposed algorithm takes image features into consideration in the selection process. Statistically, most images have regular features resulting in connected subband coefficients. Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients. In the proposed algorithm, coefficients are selected due to their magnitude, and only a subset of those selected coefficients which exhibit a spatially regular behavior remain for image reconstruction. Therefore, two thresholds are used in the coefficient selection process. The first threshold is used to distinguish coefficients of large magnitude and the second is used to distinguish coefficients of spatial regularity. The performance of the proposed wavelet denoising technique is an improvement upon several other established wavelet denoising techniques, as well as being computationally efficient to facilitate real-time image-processing applications.     

基于小波变换和相对量化的图像数字水印算法

提出了一种基于分块小波变换和相对量化的图像数字水印算法。由于在算法中采用了量化嵌入策略,从而在提取水印时无需原始图像和原始水印。实验结果表明,该算法对嵌入的水印具有很好的透明性,对亮度和对比度调整攻击具有非常强的稳健性,对JPEG压缩、重采样、颜色抖动、平滑和加噪声等常见的图像处理攻击具有很强的稳健性,并且算法思想简单、步骤简便和容易实现。     

掌形图像小波极大值预处理算法的实现

通过对掌形图像采用小波模极大值算法去除噪声,建立了极大值矩阵及跟踪矩阵,分辨出小波系数的信噪属性,剔除了噪声部分对应的小波系数极大值,从而抑制了噪声污染.在阈值设置问题上,采用改进的自适应阈值,克服单一阈值不能在每级尺度上将信号与噪声作最大分离的缺点,该方法不仅有效去除噪声,同时保持了图像边缘细节,具有良好的消除噪声的效果.