纯二维小波滤波器组及其在图像压缩中的应用

构造了一组新的纯二维不可分离的小波滤波器——全相位离散反余弦列率滤波器（APDICSF）。该滤波器具有五株排列的交错采样形式和良好的内插性能,适用于图像分解压缩。将其用于提升格式的预测和更新滤波器,得到与可分离小波滤波器组不同的图像分解方式,并用SPIHT实现图像的压缩编码。实验证明,APDICSF组的压缩效果好于经典的Neville滤波器,在图像压缩方面有良好的应用前景。     

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.     

最优内插的纯二维小波构造

研究了纯二维小波的构造,并根据二维图像各向同性的谱密度模型构造了一组最优内插滤波器,将其用于提升格式中,构造了具有最优内插效果的纯二维小波滤波器组.通过MSE和能量熵实验对比,证明这种纯二维小波滤波器内插效果和能量聚积方面好于或接近于著名Neville滤波器,可以用于图像的压缩和超分辨率.     

高正则二维小波的构造及在遥感图像编码中的应用

首先根据对称正交二维小波滤波器组的阶因式分解表示,结合正则性条件,建立一组以滤波器组参数为未知数的高阶多元多项式非线性方程组,并将这一组方程分解为两个子方程组,应用计算代数中的Groebner基算法分别求出解其Groebner基后获得二维小波滤波器组的全部参数,从而构造出一种集正交性、对称性和高正则性于一体的完美的"真"二维小波;其次从二维正交多分辨分析出发,推导出二维小波变换的分解和重构快速算法;最后将构造得到的3正则阶二维小波和SPIHT编码算法相结合对某地的遥感图像进行压缩编码.实验结果显示该方法具有较好的编码性能.     

线性完全重构的二维滤波器组的Groebner基设计

为设计线性完全重构的二维滤波器组,引用了计算代数中的Groebner基方法,根据线性相位条件和完全重构条件,分别设计出二维滤波器组的分析滤波器和综合滤波器的多相元矩阵,给出其参数化形式。根据小波构造理论,利用所设计的分析滤波器组构造出一个对称的纯二维小波。设计结果显示了Groebner基方法的有效性,设计方法更为简单。     

基于双正交小波变换的航空图像压缩研究

提出一种基于提升方案的双正交小波变换结合SPIHT编码的图像压缩方法.小波提升方案是继多分辨分析之后,另一种非常有效的构造小波滤波器的方法,在双正交条件下按所需的小波性能自由构造双正交小波基,并能加快小波变换的执行速度.分析了应用提升方案构造双正交小波的算法,选用性能优良的双正交小波,结合SPIHT编码,进行图像压缩.实验表明,通过该方法进行图像压缩,图像重建质量较高.     

二维DFT域全相位数字滤波器设计与实现

一维全相位数字信号处理已经在谱分析、自适应、模板设计、滤波器组分析与综合等领域得到应用,但对于二维全相位理论的推导很少而且没有实现的方法.因此,本文在一维全相位信号处理方法的基础上,首先系统地分析二维全相位信号处理模式,理论推导出无窗、单窗和双窗模式下传输函数表达式;其次,首次设计出二维全相位处理的系统实现框图,并对算法复杂度进行了优化分析;还有,对二维全相位信号处理的性质进行了分析;最后,借助MATLAB实现了DFT滤波器设计并画出3D特性曲面,完成设计二维DFT全相位滤波器的理论、方法和实验验证.     

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.     

基于二维APDCSF的列率子带特征编码方法

提出了一种子带编码的新方法。该方法利用二维全相位离散反余弦列率滤波器（APDCSF）对图像进行子带分解;对于低频子带图像采用直接斜交多重亚采样和基于全相位离散反余弦列牢滤波器（APDICsF）的多重旋转内插恢复．而对高频子带图像利用直方图自动阈值化提取如边缘和线等特征的图像元;根据各个子带的图像元的特征分别进行编码压缩,解压缩后利用凸集投影重建原始图像。该方法消除了传统的离散余弦变换（DCT）编码的方块化效应,与基于小波变换的子带特征编码方法相比,计算复杂度小,压缩率高,主观视觉性能好,对于灰阶图像可达到0．1～0．3bpp,特别适用于低比特率图像压缩。     

用二维带窗全相位Fourier模板恢复真彩色图像

二维带窗全相位Fourier滤波器具有通带起伏小,阻带衰减大,过渡带小等优良特点,并且滤波时可等效为一模板处理.为此,本文构造了真彩色静态图像传送与恢复处理的系统用以提供滤波所需的测试信号.在系统发送端,通过构造调制矩阵对U、V分量进行频谱搬移.在接收端采用了二维全相位带窗模板进行图像复原.实验证明用全相位模板恢复的图像的PSNR相比于非重叠方法可提高5dB以上.     

Motion compensated lossy-to-lossless compression of 4-D medical images using integer wavelet transforms.

This paper proposes a method for progressive lossy-to-lossless compression of four-dimensional (4-D) medical images (sequences of volumetric images over time) by using a combination of three-dimensional (3-D) integer wavelet transform (IWT) and 3-D motion compensation. A 3-D extension of the set-partitioning in hierarchical trees (SPIHT) algorithm is employed for coding the wavelet coefficients. To effectively exploit the redundancy between consecutive 3-D images, the concepts of key and residual frames from video coding is used. A fast 3-D cube matching algorithm is employed to do motion estimation. The key and the residual volumes are then coded using 3-D IWT and the modified 3-D SPIHT. The experimental results presented in this paper show that our proposed compression scheme achieves better lossy and lossless compression performance on 4-D medical images when compared with JPEG-2000 and volumetric compression based on 3-D SPIHT.     

二维电能质量数据压缩

近年来,大量电能质量监测点的建立,使得电能质量数据增加,为处理大量的电能质量监测数据,提出了一种基于方向小波变换的电能质量数据压缩方法.将一维的电能质量数据转换成二维的数据,得到经过此变换分解的二维数据的小波系数,再将这些小波系数应用图像压缩中的SPIHT（多级树集合分裂）编码算法,压缩二维表示的电能质量数据.实验结果表明,此方法具有压缩率高、速度快,控制压缩比和特征不变的特点,有利于根据网络的状况调节传输的数据量.     

一种新颖的中子辐照图像压缩算法

分析了中子辐照图像的持点,提出一种适合于数字中子照相系统的图像压缩算法。将小波和方向滤波器组（DFB）结合,得到非冗余的图像变换WDFB;通过WDFB系数的重新组合,提出一种新颖的图像压缩方案,该方案类似于无表零树编码（LZC）算法;为了充分利用WDFB变换对二维分段光滑函数的良好表达能力,该算法引入线性索引技术并采用不同于LZC算法的零树表达策略。实验结果表明,该算法用于中子辐照图像压缩是有效的,在相同压缩比下,其主观视觉质量和峰值信噪比（PSNR）都明显优于LZC算法,和多级树集合分裂编码（SPIHT）算法相比,具有相当的压缩性能但更易于硬件实现。     

基于结合小波和脊波的分层图像压缩算法

基于分层思想,结合小波变换和脊波变换各自的优势,提出了一种新型的分层图像编码算法。首先使用一种各向异性扩散平滑方法,原始图像I经过平滑,得到平滑图像R1,然后用原始图像I减去平滑图像R1得到纹理图像R2。对平滑图像R1采用多级树集合分裂算法(Set partitioning in hierarchical trees,SPIHT)编码;对纹理图像R2进行改进的正交有限脊波变换,然后采用相应改进的SPIHT算法编码。实验结果表明,本算法在同等码率的情况下优于SPIHT算法的压缩性能。对于纹理比较丰富的图像,效果尤为明显。     

用整数小波结合改进SPIHT编码实现静止图像无损压缩

无损图像压缩是图像处理中的一个重要领域 ,传统的基于熵编码的无损压缩编码方式存在一定的局限。利用提升算法构造整数小波变换 ,然后针对无损压缩特点 ,对 SPIHT算法加以改进 ,实现了一种可行的图像无损压缩编码方案。实验表明 ,优于传统熵编码和直接采用 SPIHT的无损压缩方法     

Embedded foveation image coding

The human visual system (HVS) is highly space-variant in sampling, coding, processing, and understanding. The spatial resolution of the HVS is highest around the point of fixation (foveation point) and decreases rapidly with increasing eccentricity. By taking advantage of this fact, it is possible to remove considerable high-frequency information redundancy from the peripheral regions and still reconstruct a perceptually good quality image. Great success has been obtained previously by a class of embedded wavelet image coding algorithms, such as the embedded zerotree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) algorithms. Embedded wavelet coding not only provides very good compression performance, but also has the property that the bitstream can be truncated at any point and still be decoded to recreate a reasonably good quality image. In this paper, we propose an embedded foveation image coding (EFIC) algorithm, which orders the encoded bitstream to optimize foveated visual quality at arbitrary bit-rates. A foveation-based image quality metric, namely, foveated wavelet image quality index (FWQI), plays an important role in the EFIC system. We also developed a modified SPIHT algorithm to improve the coding efficiency. Experiments show that EFIC integrates foveation filtering with foveated image coding and demonstrates very good coding performance and scalability in terms of foveated image quality measurement.     

A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT

Due to its excellent rate–distortion performance, set partitioning in hierarchical trees (SPIHT) has become the state-of-the-art algorithm for image compression. However, the algorithm does not fully provide the desired features of progressive transmission, spatial scalability and optimal visual quality, at very low bit rate coding. Furthermore, the use of three linked lists for recording the coordinates of wavelet coefficients and tree sets during the coding process becomes the bottleneck of a fast implementation of the SPIHT. In this paper, we propose a listless modified SPIHT (LMSPIHT) approach, which is a fast and low memory image coding algorithm based on the lifting wavelet transform. The LMSPIHT jointly considers the advantages of progressive transmission, spatial scalability, and incorporates human visual system (HVS) characteristics in the coding scheme; thus it outperforms the traditional SPIHT algorithm at low bit rate coding. Compared with the SPIHT algorithm, LMSPIHT provides a better compression performance and a superior perceptual performance with low coding complexity. The compression efficiency of LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information belonging to the lower frequency bands earlier in the bit stream than that of the SPIHT to better exploit the energy compaction of the wavelet coefficients. HVS characteristics are employed to improve the perceptual quality of the compressed image by placing more coding artifacts in the less visually significant regions of the image. Finally, a listless implementation structure further reduces the amount of memory and improves the speed of compression by more than 51% for a 512×512 image, as compared with that of the SPIHT algorithm.     

Wavelet tree classification and hybrid coding for image compression

A hybrid coding system that uses a combination of set partition in hierarchical trees (SPIHT) and vector quantisation (VQ) for image compression is presented. Here, the wavelet coefficients of the input image are rearranged to form the wavelet trees that are composed of the corresponding wavelet coefficients from all the subbands of the same orientation. A simple tree classifier has been proposed to group wavelet trees into two classes based on the amplitude distribution. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ. Experimental results show that advantages obtained by combining the superior coding performance of VQ and efficient cross-subband prediction of SPIHT are appreciable for the compression task, especially for natural images with large portions of textures. For example, the proposed hybrid coding outperforms SPIHT by 0.38 dB in PSNR at 0.5 bpp for the Bridge image, and by 0.74 dB at 0.5 bpp for the Mandrill image.     

A 2-D ECG compression method based on wavelet transform and modified SPIHT

A two-dimensional (2-D) wavelet-based electrocardiogram (ECG) data compression method is presented which employs a modified set partitioning in hierarchical trees (SPIHT) algorithm. This modified SPIHT algorithm utilizes further the redundancy among medium- and high-frequency subbands of the wavelet coefficients and the proposed 2-D approach utilizes the fact that ECG signals generally show redundancy between adjacent beats and between adjacent samples. An ECG signal is cut and aligned to form a 2-D data array, and then 2-D wavelet transform and the modified SPIHT can be applied. Records selected from the MIT-BIH arrhythmia database are tested. The experimental results show that the proposed method achieves high compression ratio with relatively low distortion and is effective for various kinds of ECG morphologies.     

A fast and efficient hybrid fractal-wavelet image coder.

The excellent visual quality and compression rate of fractal image coding have limited applications due to exhaustive inherent encoding time. This paper presents a new fast and efficient image coder that applies the speed of the wavelet transform to the image quality of the fractal compression. Fast fractal encoding using Fisher's domain classification is applied to the lowpass subband of wavelet transformed image and a modified set partitioning in hierarchical trees (SPIHT) coding, on the remaining coefficients. Furthermore, image details and wavelet progressive transmission characteristics are maintained, no blocking effects from fractal techniques are introduced, and the encoding fidelity problem common in fractal-wavelet hybrid coders is solved. The proposed scheme promotes an average of 94% reduction in encoding-decoding time comparing to the pure accelerated Fractal coding results. The simulations also compare the results to the SPIHT wavelet coding. In both cases, the new scheme improves the subjective quality of pictures for high-medium-low bitrates.