基于DCT系数小波重组的图像压缩算法

本文提出一种与小波变换结构近似的嵌入式编码-SADSPIHT.将图像8*8子块进行DCT变换后的系数按照小波变换结构重新组合,运用误差和去除法降低低频段系数的冗余度.之后,运用基于分层树集合分割排序的编码算法对图像进行压缩编码.实验表明,采用SADSPIHT处理的图像,在相同压缩比的情况下,与其他编码方法相比,图像质量得到改善.当码率为1.00bpp时,EZW,MSEDCT,SPIHT,SADSPIHT,峰值信噪比分别为39.55dB,39.75dB,39.97dB,40.17dB.     

一种结合Contourlet和小波变换的图像编码算法

该文提出了一种Contourlet变换和小波变换相结合,使用新的空间方向树的类似SPIHT编码算法。该算法先对图像进行Contourlet变换,再对变换后的低频子带进行多级小波变换,然后根据变换后系数的结构特性,借鉴小波SPIHT编码思想,构造了一种新的空间方向树,实现了对变换后系数的类似SPIHT编码。仿真实验结果表明,该算法与小波变换,Contourlet变换和基于小波的Contourlet变换的SPIHT算法相比,重构图像保留了更多的纹理和细节信息,并且在低比特率下具有较高的峰值信噪比。     

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。     

针对静止图像压缩的整数小波优化设计

为了提高整数小波变换(IWT)的有损图像压缩效率,提出一种基于最优缩放因子(OSF)的整数小波优化设计方案——OSF-IWT。通过对变换前图像系数的适当放大2的整数幂倍,减少尾数截断对变换系数幅值的影响。同时选取提升框架中的OSF,优化小波系数的分布特性,提高小波图像的能量集中性,使有损压缩效果达到最佳。采用SPIHT编码的实验结果表明,在保持原IWT的低计算复杂度和可逆性同时,有损编码条件下,OSF-IWT的峰值信噪比(PSNR)超过采用Daubechies(Db)917的离散小波变换(DWT)0．3～0．6dB。     

基于四叉树分类的小波图像二维网络编码量化

本文提出了一种小波图像在进行小波系数四叉树分类后运用二维网格编码量化（2D-TCQ）的新方法。首先根据小波图像中各子带系数的带间相关性对其进行四叉树分类,然后在扩展的二维码书空间对重要类系数进行网格编码量化,运用维特比算法寻找最优量化序列,最终形成有序的嵌入式编码比特流。仿真结果表明,该方法在相同编码率下与SPIHT算法相比,PSNR获得了0.4dB左右的改善;使用小一倍的码书,相同编码率下比四叉树分类后使用一维TCQ获得了0.1dB左右的改善。由于本方法可以采用较小的码书尺寸,计算量较小,所以适用于低存贮、低功耗的编解码环境。     

分布式压缩的图像关联重建方法

针对通信网络有限带宽传输大数据量图像信息的瓶颈问题,提出了基于系数整合法的分布式压缩传输的图像重建新方法,首先通过对原图像进行分块小波变换获得子图像小波系数向量,从而大大降低压缩编码系统的数据传输量,再对压缩编解码的子图像小波系数进行关联处理,形成原图像的整体小波变换系数向量,最后采用小波反变换实现原图像的重构.仿真分...     

提升小波变换与分形结合的图像压缩算法

遥感图像在环境监测、军事侦察等多方面有着广泛应用,然而遥感图像包含信息量大,对其进行压缩来提高存储效率具有重要意义.传统分形编码由于压缩比大的特点被广泛应用到遥感图像压缩中,但是传统分形编码存在压缩时间太长的问题.提出提升小波变换与改进分形结合的压缩方法,把提升小波变换后的低频分量进行基于最小方差搜索法的分形压缩.实验结果表明,提升小波变换与改进的分形结合的压缩方法与小波变换与分形结合的压缩方法相比,在峰值信噪比保持在35 dB不变的情况下,压缩时间大约可以缩短8倍,图像压缩比也有提高.     

基于改进的K-L变换的多光谱图像压缩算法

融合离散小波变换(DWT,discrete wavelet tran sform)与Karhunen-Loeve变换(KLT),将图像的能量集中到少数系数上,以达到更好的 压缩效果。首先将多光谱图像的每个谱段进行快速9/72D DWT,消除多光谱图像的大部分 空间冗余;然后对所有谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空 间冗余;最后对所得谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空间冗 余;最后对所得系数进行熵编码,得到压缩码流。实验结果表明,在码率为0.25～2.0bit/ pixel范围内,平均信噪比(SNR)高于41dB,同时缩短了运 算时间,从而提升了多光谱图像压 缩算法的性能。     

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。     

基于整数小波变换的无人机侦查图像的压缩

基于传统小波变换过程复杂的特点和SPIHT编码算法过程重复运算、存储量大,并且侦查图像的目标像素小且目标数量大,相关性低,提出了一种基于差分脉冲编码调制和整数小波变换相结合的改进混合编码算法。首先以压缩效果为准则,以侦查图像为标准训练图像,以压缩比、峰值信噪比为参数来确定最优的小波基。然后以（9,7）整数提升小波对图像进行分解,对低频子带的重要系数先进行DPCM编码。最后对整个变换后的子带进行SPIHT编码。实验结果表明该算法在相同的比特率下得到的重构图像的PSNR值高于原算法,而且侦查图像的低频近似效果得到了改善。     

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.     

Significance-linked connected component analysis for wavelet imagecoding

The success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256x256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.     

一种基于低复杂度嵌入零块编码的遥感图像压缩编码方法

该文针对遥感图像的数据特点,提出了一种新的遥感图像编码方法。它基于一种改进的小波变换嵌入零块编码算法。新算法中改进了零块编码中四叉树分裂算法(quadtree),并设计亍新的链表生成和不重要集合排序策略。通过这些方法的改进,不仅提高了图像编码性能,同时还大大提高了运算效率。实验表明该文阐述的算法具有很低的复杂度和高的压缩率,PSNR和计算速度均超过SPIHT和SPECK。在1bpp下,该文方法的PSNR比SPIHT提高了0.3dB以上,计算速度比SPIHT提高了35％。     

自适应分割的雷达回波数据混合编码算法

该文提出了一种基于自适应分割模型的雷达信号混合编码算法。首先用自适应高斯混合体模型将雷达回波数据分为两类;一类是由运动目标组成的前景图;一类是视野中静止(如礁石等)或具有重复性运动目标(如海浪等)的背景图。然后对前景图和背景图分别用整数小波去相关,对变换后的前景图和背景图分别采用改进后的SPIHT无损压缩和有损压缩编码。实验表明,此编码算法可有效降低数据传输率,在相同传输码率下,相对于不分割直接压缩算法EZW和SPIHT,重构信号的PSNR增加0.3-0.5dB,主观质量也优于同类算法。     

A low memory zerotree coding for arbitrarily shaped objects

The set partitioning in hierarchical trees (SPIHT) algorithm is a computationally simple and efficient zerotree coding technique for image compression. However, the high working memory requirement is its main drawback for hardware realization. We present a low memory zerotree coder (LMZC), which requires much less working memory than SPIHT. The LMZC coding algorithm abandons the use of lists, defines a different tree structure, and merges the sorting pass and the refinement pass together. The main techniques of LMZC are the recursive programming and a top-bit scheme (TBS). In TBS, the top bits of transformed coefficients are used to store the coding status of coefficients instead of the lists used in SPIHT. In order to achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. A compact emplacement of the transformed coefficients is also proposed to further reduce working memory. The LMZC carefully treats "don't care" nodes in the wavelet tree and does not use bits to code such nodes. Comparison of LMZC with SPIHT shows that for coding a 768 /spl times/ 512 color image, LMZC saves at least 5.3 MBytes of memory but only increases a little execution time and reduces minor peak signal-to noise ratio (PSNR) values, thereby making it highly promising for some memory limited applications.     

基于DCT变换的内嵌静止图像压缩算法

提出了一种有效的基于离散余弦变换(DCT)的内嵌子带图像编码算法.Xiong等人提出的EZDCT算法采用零树结构实现了一种内嵌DCT编码器,且其性能优于JPEG.本文指出DCT的零树结构在内嵌DCT算法中并非很有效,同时提出了一种不依赖零树结构的简便、高效的内嵌DCT子带编码算法.实验结果表明本文算的压缩性能(PSNR)比EZDCT高约0.5~1.5dB,且接近当前最通用的内嵌小波SPIHT算法,在对某些图像压缩时还优于SPIHT算法.     

Shape adaptive wavelet transform for magnetic resonance images coding

Region-based coding is an important feature in today's image coding techniques as it follows different regions of the image that will be encoded at different bit rates and hence at different qualities rather than encoding the entire image with a single quality constraints. This article proposes an algorithm for the region-based coding of the brain magnetic resonance images in which the brain part will be encoded with more number of bits than the background. This method employs Shape Adaptive Discrete Wavelet Transform, which can transform the regions of interest and the background on the images independently and the coefficients can be encoded by using the SPIHT coding at different levels. This algorithm was compared with the existing wavelet-based coding techniques and a better PSNR was achieved for the same bit rate by reconstructing the region of interest with high quality than the background.     

基于Brushlet变换的SAR图像SPIHT压缩算法

提出一种基于Brushlet多方向变换特点的SPHIT编码算法,并将其应用于SAR图像压缩编码中,通过对真实SAR图像的压缩实验表明,相比于传统的小波编码方法,Brushlet变换域的SPHIT算法能获得更好的压缩性能,提高了解码后的PSNR值,并且较好地保留了图像细节特征和提高了可视性.     

纯二维全相位滤波器组及其在图像压缩中的应用

基于全相位理论,设计了3种纯二维全相位滤波器组(ADFB).这些滤波器组可以直接对图像进行纯二维分解和重构,若忽略滤波器的有限精度问题,系统是全重建的.为了减小运算量,纯二维ADFB采用提升格式实现.仿真试验表明,纯二维ADFB具有良好的数据压缩特性.在压缩率相同的情况下,尤其是IDCT-AFB7.7比Daubechies9/7小波重建图像的峰值信噪比(PSNR)最多小0.7 dB,这与采用的适合于可分离二维小波变换的四叉树SPIHT编码方案有关,若采用二叉树SPIHT编码,会使压缩效果得到改善.