一种零树与游程相结合的小波图象编码方法

Shapiro的零树编码方法是众多小波图象编码方法中的佼佼者 ,但通过研究发现 ,图象的小波系数经过量化和零树编码后 ,如果按一定的顺序扫描 ,则零树根会连续出现 ,因此 ,若对 Shapiro的零树编码方法进行改进 ,即在零树编码之后 ,熵编码之前 ,对连续排列的零树根进行游程编码 ,则可以进一步提高图象编码的压缩比 .理论分析和实验结果均表明 。

基于零树、金字塔格型矢量量化的小波图象编码

近年来 ,金字塔格型矢量量化 (PL VQ)方法由于其本身的特点和性质引起了人们的广泛兴趣 ,而文献 [1]介绍的基于零树 (ZR)的编码也获得了极大的成功 .根据信号的二进小波分解特点和小波图象系数的分布特点 ,利用 D4格将 PL VQ和零树结合起来 ,提出了一种基于零树和金字塔格型矢量量化的小波图象编码方法 ,该方法首先采用金字塔格型矢量方法来量化小波图象系数 ,以得到非零格点和零格点 ;然后采用复合熵编码来处理非零格点 ;最后为了有效确定非零格点的位置 ,也就是为了有效地处理零格点 ,又引进了重要图的概念 .在此基础上 ,从下往上、从上往下二次扫描重要图 ,再采用改进的零树编码方法来处理零格点 .实验结果证明 ,本文算法优于传统的基于游长的熵编码 .

多小波图象编码

首先 ,与许多有关多小波的文献不同 ,该文从 Mallat多分辨率分析的角度叙述多小波理论 ,并由此推导出多小波变换的快速算法 ;其次 ,结合二种常用的多小波图象变换预处理方法 ,提出了一种新的预处理方法 .最后 ,揭示了经多小波分解后 ,同一幅矢量子带图象不同分量图之间的相关性 ,并利用这种相关性进行图象编码 .理论分析和实验数据都表明 ,多小波图象编码是一种很有潜力的图象编码方法 .

小波图象编码中的对称边界延拓法

在小波变换图象编码中 ,由于图象信号是有限长信号 ,在子带分解过程中信号边界将外延 ,因此为满足完全重构条件 ,且不增加信号的样本总数 ,必须对信号边界进行延拓 .该文对小波变换图象编码中的对称周期延拓法进行了论述 ,并叙述了其原理及实现方法 ,还讨论了如何根据滤波器长度为奇数或偶数的不同 ,相应地延拓输入信号 ,并恰当地截取出所需长度的子带信号 .同时 ,讨论了综合时如何对子带信号进行延拓的问题 .

Lifting Scheme及其在小波图象编码中的应用

L ifting Schem e是构造第二代小波的关键技术 .相对于第一代小波而言 ,L ifting Scheme是一种比 Mallat算法更快、更简单和更容易操作的算法 ,也是 JPEG2 0 0 0推荐的算法 .为了将其应用到小波图象编码中 ,提出了一种对 L ifting Scheme作适当改进以用于小波图象编码的方法 .该方法就是先用 L ifting Scheme来实现 D9/ 7双正交小波变换 ,然后再用这种技术实现的 D9/ 7双正交小波变换来进行图象压缩编码 .在将 L ifting Schem e算法用于小波图象编码的过程中 ,对该算法做了必要的简化 ,以便保证每个提升 (lifting)环节都是 FIR滤波 .同时 ,根据能量守恒的原则 ,重新调整了尺度因子 .实验结果表明 ,这种经过改进的 L ifting Schem e取得了比

小波图象的视觉特征探讨

对小波图象的视觉特征作了某些心理特征的讨论,并对它的实验图象进行验证分析。结果表明小波图象有很好的人眼视觉特性。

基于小波系数零树结构的分形预测图象编码

提出了一种基于小波系数零树结构的分形预测图象编码方法 ,首先按照零树结构在同方向不同分辨率的各个子带图象上确定要预测的图象块 (Range Block) ,也就是将这些图象块串起来构成一棵与零树相类似的图象树(Range Tree) ;然后 ,对每一图象块 ,在同方向低一级分辨率的子带图象上寻找与其最佳分形匹配的相似块(Dom ian Block) ,而这些相似块也按照零树结构上下延伸 ,以产生多棵相似树 (Dom ain Tree) ,其中与图象树最接近的相似树就是图象树的预测树 .理论分析和实验结果表明 ,该方法在峰值信噪比稍微下降的情况下 ,能够获得较高的压缩比 .

小波分形混合图象编码

该文在收集和阅读了与小波分形混合图象编码方法有关文献的基础上 ,对小波分形混合图象编码方法的研究现状和发展前景进行了尽可能全面和详细的评述 .文中介绍了小波图象编码经过 10年研究 ,主要算法已经基本成熟 ,并受到一些正在酝酿之中的图象编码国际标准的青睐 ,而分形图象编码尚处于学术研究阶段 ,并指出小波分形混合图象编码是分形图象编码从学术研究走向实际应用的出路之一 .

基于方向剖分的小波域分形图象压缩

该文提出一种新的分形和小波混合编码方法 -基于方向剖分的小波域分形图象压缩方法 :在将图象分解到小波域后 ,对各子图象 ,根据其所占能量的大小和所代表的方向纹理信息 ,采用不同大小、形状与类别的定义域块和值域块进行分形编码。经实验证明 ,这种基于方向剖分的小波域分形编码方法 ,在保证一定的还原图象质量的前提下 ,可以大大地提高了分形编码的速度 ,如果结合多层小波分解的分形压缩编码的预测法和零树法 ,还能进一步提高压缩率 。

基于JPEG2000的图象局部可分级检索

提出了一种基于 JPEG2 0 0 0的图象局部可分级检索方法 .首先 ,对图象采用 JPEG2 0 0 0的基本算法进行压缩编码 ;然后 ,把压缩码流按零树结构编排 ,每个零树结构对应着图象的某个网格区域 ,从零树结构所包含的 DWT系数可以分级重建这个网格区域的内容 .检索时 ,先从图象的最低分辨率子图象 (即 L L 子图象 )的预览开始 ,一旦发现目标 ,则锁定目标 ;然后在空间分辨率和信噪比两个方面逐级浮现目标所在网格的内容 .由于该方法仅仅使用部分压缩码流数据来恢复图象的局部内容 ,从而节省了大量的计算资源 ;最后给出了基于 JPEG2 0 0

The Segmentation of FMI Image Based on 2-D Dyadic Wavelet Transform

A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get images of pores, vugs and fractures. A segmentation method based on the dyadic wavelet transform in 2-D is introduced in this paper. The first step is to find all the edge pixels of the FMI image using the 2-D wavelet transform. The second step is to calculate a segmentation threshold based on the average value of the edge pixels. Field data processing examples show that sub-images of vugs and fractures can be correctly separated from original FMI data continuously and automatically along the depth axis. The image segmentation lays the foundation for in-situ parameter calculation.

Efficient Quadtree based Fractal Image Coding Scheme in Wavelet Transform Domain *

This paper presents an efficient quadtree based fractal image coding scheme in wavelet transform domain based on the wavelet based theory of fractal image compression introduced by Davis. In the scheme, zerotrees of wavelet coefficients are used to reduce the number of domain blocks, which leads to lower bit cost required to represent the location information of fractal coding, and overall entropy constrained optimization is performed for the decision trees as well as for the sets of scalar quantizers and self quantizers of wavelet subtrees. Experiment results show that at the low bit rates, the proposed scheme gives about 1 dB improvement in PSNR over the reported results.

Mean shift based log-Gabor wavelet image coding

In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.

A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM

In order to enhance the image information from multi-sensor and to improve the abilities of the information analysis and the feature extraction, this letter proposed a new fusion approach in pixel level by means of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequency band and high frequency band in higher scale. It offers a more precise method for image analysis than Wavelet Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform to obtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. Then WPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observation de la Therre ) image into low frequency band and high frequency band in three levels. Next, two high frequency coefficients and low frequency coefficients of the images are combined by linear weighting strategies. Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approach can fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM (Histogram Matched)-based fusion algorithm and WT-based fusion approach.

MULTI-SPECTRAL AND HYPERSPECTRAL IMAGE FUSION USING 3-D WAVELET TRANSFORM

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.

Mean shift texture surface detection based on WT and COM feature image selection

Mean shift is a widely used clustering algorithm in image segmentation. However, the segmenting results are not so good as expected when dealing with the texture surface due to the influence of the textures. Therefore, an approach based on wavelet transform (WT), co-occurrence matrix (COM) and mean shift is proposed in this paper. First, WT and COM are employed to extract the optimal resolution approximation of the original image as feature image. Then, mean shift is successfully used to obtain better detection results. Finally, experiments are done to show this approach is effective.

More efficient ground truth ROI image coding technique: implementation and wavelet based application analysis

In this paper, more efficient, low-complexity and reliable region of interest （ROI） image codec for compressing smooth low texture remote sensing images is proposed. We explore the efficiency of the modified RO! codec with respect to the selected set of convenient wavelet filters, which is a novel method. Such ROI coding experiment analysis representing low bit rate lossy to high quality lossless reconstruction with timing analysis is useful for improving remote sensing ground truth surveillance efficiency in terms of time and quality. The subjective [i.e. fair, five observer （HVS） evaluations using enhanced 3D picture view Hyper memory display technology] and the objective results revealed that for faster ground truth ROI coding applications, the Symlet-4 adaptation performs better than Biorthogonal 4.4 and Biorthogonal 6.8. However, the discrete Meyer wavelet adaptation is the best solution for delayed ROI image reconstructions.

Listless zerotree image compression algorithm

In this paper,an improved zerotree structure and a new coding procedure are adopted,which improve the reconstructed image qualities. Moreover, the lists in SPIHT are replaced by flag maps, and lifting scheme is adopted to realize wavelet transform, which lowers the memory requirements and speeds up the ceding process. Experimental results show that the algorithm is more effective and efficient compared with SPIHT.

Selective image enhancement method for low energy target information area

A selective subband enhancement method based on biorthogonal wavelet base is proposed. This novel image enhancement method is just for those images in which the energy of target information area is relatively lower. It includes two parts: one is enhancing the low frequency subband by wavelet decomposition and the other is building a new criterion based on entropy window to image evaluation. Experimental results show that this new scheme may result in a perfect image processing.

OPTIMAL WAVELET FILTER DESIGN FOR REMOTE SENSING IMAGE COMPRESSION

A new approach for designing the Biorthogonal Wavelet Filter Bank (BWFB) for the purpose of image compression is presented in this letter. The approach is decomposed into two steps. First, an optimal filter bank is designed in theoretical sense based on Vaidyanathan’s coding gain criterion in SubBand Coding (SBC) system. Then the above filter bank is optimized based on the criterion of Peak Signal-to-Noise Ratio (PSNR) in JPEG2000 image compression system, resulting in a BWFB in practical application sense. With the approach, a series of BWFB for a specific class of applications related to image compression, such as remote sensing images, can be fast designed. Here, new 5/3 BWFB and 9/7 BWFB are presented based on the above approach for the remote sensing image compression applications. Experiments show that the two filter banks are equally performed with respect to CDF 9/7 and LT 5/3 filter in JPEG2000 standard; at the same time, the coefficients and the lifting parameters of the lifting scheme are all rational, which bring the computational advantage, and the ease for VLSI implementation.