一种基于小波系数重要图编码的图像压缩算法

提出了一种新的基于小波系数重要图编码的图像压缩算法。该算法根据量化后的波系数的特点进行了一种期望排序，然后舍掉序列后面大量的零值小波系数，从而得到一个波系数子集，能以少的小波系数来很好地逼近原始图像，省去了零树编码中零树结构带来的大量比特开销。实验表明，该算法与MPEG-4的静止图像压缩算法相比较，重构图像的峰值信噪比（PSNR）值在相同码率下有较大的提高。     

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     

改进SPIHT算法对医学图像的压缩

医学图像是医学诊断和疾病治疗的重要根据。为了实现图像的存储和远程医疗中快速传输图像的要求,必须对图像进行压缩。先分析CT医学图像经过小波变换后系数的统计特性,在基于小波变换的基础上,用SPIHT算法对CT医学图像进行压缩编码。提出了2种针对CT医学图像压缩编码改进的SPIHT算法。一是细化扫描产生的有效值,二是将小波变换后系数的低频近似部分按二进比特进行传输。用Matlab进行仿真,仿真结果表明改善了峰值信噪比。     

小波分析在图像融合中的应用

图像融合的目的是把同一目标的多幅图像融合成一幅质量高的图像。本文给出了一种基于小波变换的图像融合方法，首先对两幅图像使用正交小波进行分解，得到两幅图像的小波分解系数，根据小波系数的特点，对两幅图像的小波系数进行比较，取绝对值大的小波系数构成融合的小波系数矩阵。对融合小波系数矩阵进行小波反变换，得到两幅图像的融合图像。最后使用该方法对两幅图像进行了融合，得到的融合图像汲取了两幅图像的优势，是一幅质量高的图像。     

图像小波变换实现及EZW编码理论研究

根据图形小波知识,对于图形小波变换实现采取的技术—这个Mallat算法给以深入研究,同时基于Mallat算法采取VC++编程的方法来对图像小波变换进行分析与研究。Mallat算法是将信号处理中的滤波器和计算小波系数联系在一起,组建成塔形分解算法,这样小波系数相关计算简单化了。嵌入式小波零树系数编码(EZW)是将小波变换的各子带系数表示为零树结构。EZW是一种简单而有效的图像编码算法。使用这种算法,编码者可在任一点结束编码,所以允许精确达到一个目标比率,而这时仍能产生同样的图像。有很好空间的小波变换——频域局部特性,关于图像这个可以有效地进行处理。     

基于双正交小波变换的图像压缩编码

提出了一种基于双正交小波分解的静态图像压缩编码方法,将图像作３次小波分解后,对低频变换系数作ＤＰＣＭ编码,对高频变换系数采用游程编码。实验证明这是一种比较有效的编码方法。     

图像小波域视觉重要性顺序模型比特零树预测熵编码

本文研究一种静态图像小波（Ｗａｖｅｌｅｔ）域系数压缩方法，可以在任意给定比特数时获得一定视觉意义下的最佳图像质量。该方法根据图像小波分解和人类视觉（ＨＶＳ）的特点及其关系，对系数进行不同间隔的量化，并规定系数的视觉重要性顺序，结合零树数据结构，对系数进行该顺序的比特层零树扫描和预测，输出符号数据流，最后用自适应算术熵编码实现高效率编码。该方法易于用ＶＬＳＩ实现，输出比特率任意可调。计算机模拟结果显示，对于图像（Ｌｅｎａ２５６）在０．２～０．３ｂｉｔ／ｐｉｘｅｌ时仍可获得较满意的重构图像质量     

基于细节信息保护的SAR图像压缩

富含细节、边缘及纹理的SAR图像中重要信息大量集中于高、中频。基于小波变换的压缩方法往往视信号的高、中频部分为不重要信息，造成细节失真。我们使用更为精细的时频分析法即小波包(WP)分析，对信号的高频部分也作进一步分解以保存有用的高、中频信息。同时利用小波包分解系数的带内相关性将其分为大小及重要性不同的子块，进而对各子块实行不同精度的量化编码。在WP变换前先对原图像作“区域分割”以得到背景和目标物的粗略分布，从而针对不同的图像区域进行不同程度的压缩。实验结果表明，这种方法能较好地适应SAR图像的能量分布特点，获得较高的PSNR和重建图像质量。     

针对小波域量化隐藏方法的图像监测技术研究

基于对图像小波变化后各子带系数的不同统计特性，提出了一种针对小波域信息隐藏算法的新型图像隐写术分析技术。通过对原始图像和隐藏信息后的图像的小波变换系数子带直方图的分析，得出了小波域信息隐藏对子带系数的改变特性。进一步对系数进行傅立叶变换，从而量化信息隐藏对系数的影响程度。实验结果表明该方法适用于小波域量化隐藏信息算法的检测。     

采用阶梯量化优化的SPECK算法研究

通过对图像小波变换系数的分析，根据SPECK算法对较高频子带上重要系数的编码问题，提出了一种采用阶梯量化优化的SPECK 算法QSPECK，以优化SPECK算法的编码效率。即先对小波系数矩阵的较高频子带进行阶梯量化，再对量化后的矩阵进行块编码，最后进行逐次逼近量化获得嵌入式码流。实验证明：此算法优化了编码效率，提高了PSNR值。     

Dilation-run wavelet image coding

The run-length coding and the morphological representation are two classical schemes for wavelet image coding. The run-length coders have the advantage of simplicity by recording the lengths of zero-runs between significant wavelet coefficients but at the expense of yielding an inferior rate-distortion performance. The morphology-based coders, on the other hand, utilize the morphological dilation operation to delineate the clusters of significant coefficients for improving coding performance. In this paper, a novel dilation-run image coding algorithm is developed by taking the advantages of both schemes, in which the clustered significant coefficients are extracted by using the morphological dilation operation and the insignificant coefficients between the extracted clusters are coded by using the run-length coding method. The proposed dilation-run image coder is implemented in the framework of bitplane coding for producing embedded bitstreams. Compared with several state-of-the-art wavelet image coding methods, the proposed dilation-run image coding method achieves comparable rate-distortion coding performance, especially more attractive for fingerprint type of imageries.     

Efficient sign coding and estimation of zero-quantized coefficients in embedded wavelet image codecs

Wavelet transform coefficients are defined by both a magnitude and a sign. While efficient algorithms exist for coding the transform coefficient magnitudes, current wavelet image coding algorithms are not as efficient at coding the sign of the transform coefficients. It is generally assumed that there is no compression gain to be obtained from entropy coding of the sign. Only recently have some authors begun to investigate this component of wavelet image coding. In this paper, sign coding is examined in detail in the context of an embedded wavelet image coder. In addition to using intraband wavelet coefficients in a sign coding context model, a projection technique is described that allows nonintraband wavelet coefficients to be incorporated into the context model. At the decoder, accumulated sign prediction statistics are also used to derive improved reconstruction estimates for zero-quantized coefficients. These techniques are shown to yield PSNR improvements averaging 0.3 dB, and are applicable to any genre of embedded wavelet image codec.     

Image coding based on wavelet transform and uniform scalar dead zone quantizer

In this paper, a new wavelet transform image coding algorithm is presented. The discrete wavelet transform (DWT) is applied to the original image. The DWT coefficients are firstly quantized with a uniform scalar dead zone quantizer. Then the quantized coefficients are decomposed into four symbol streams: a binary significance map symbol stream, a binary sign stream, a position of the most significant bit (PMSB) symbol stream and a residual bit stream. An adaptive arithmetic coder with different context models is employed for the entropy coding of these symbol streams. Experimental results show that the compression performance of the proposed coding algorithm is competitive to other wavelet-based image coding algorithms reported in the literature.     

Wavelet image coding using variable blocksize vector quantization with optimal quadtree segmentation

In this paper, we propose an image coding scheme by using the variable blocksize vector quantization (VBVQ) to compress wavelet coefficients of an image. The scheme is capable of finding an optimal quadtree segmentation of wavelet coefficients of an image for VBVQ subject to a given bit budget, such that the total distortion of quantized wavelet coefficients is minimal. From our simulation results, we can see that our proposed coding scheme has higher performance in PSNR than other wavelet/VQ or subband/VQ coding schemes.     

Efficient SPIHT-based embedded colour image coding techniques

Proposed are two novel composite spatial orientation trees for SPIHT-based colour image codecs to link wavelet coefficients of three colour planes together. The simulation results show the improved performance of the proposed methods over the conventional wavelet-based colour image coding techniques     

差错信道下基于框架扩展的高效多描述图像编码

由量化框架扩展系数所产生的多描述图像编码,在差错信道下具有良好的图像恢复性能。该文提出一种基于框架扩展的高效多描述图像编码。首先构造一种在量化噪声下具有均方误差意义上最优框架重建的均匀紧支框架。然后以小波零树作为框架扩展的基本运算单元,在小波域对不同子带信息采用不同维度的框架进行扩展。通过不同维度的框架扩展实现对不同子带信息的不同保护。框架扩展后的数据经量化编码形成多描述码流。实验结果证明,相较于其他几种基于框架扩展的方法,该文方法在同样的信道条件下具有更好的图像差错恢复性能。     

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).     

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     

On exploiting geometric constraint of image wavelet coefficients

We investigate the problem of how to exploit the geometric constraints of edges in wavelet-based image coding. The value of studying this problem is the potential coding gain brought by improved probabilistic models of wavelet high-band coefficients. Novel phase shifting and prediction algorithms are derived in the wavelet space. It is demonstrated that after resolving the phase uncertainty, high-band wavelet coefficients can be better modeled by biased-mean probability models rather than the existing zero-mean ones. In lossy coding, the coding gain brought by the biased-mean model is quantitatively analyzed within the conventional DPCM coding framework. Experimental results show that the proposed phase shifting and prediction scheme improves both the subjective and objective performance of wavelet-based image coders.