多维多分辨率分析和图像序列的三维子波变换压缩编码

最近有文献报道图像序列的三维子波变换压缩编码。本文讨论了多维多分辨率分析和三维子带系统完全重构的充分必要条件，我们对8帧图像序列进行三级三维子波变换，然后进行零树量化和熵编码，文中给出了不同压缩比下的信噪比。编码器可以在要求的任意压缩比下停止编码．如同图像的二维子波零树编码。     

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。     

基于空间组合矢量分类的小波图像网格编码矢量量化

本文基于零树编码、矢量分类和网格编码量化的思想,提出了对小波图像采用空间矢量组合和分类后进行网格编码矢量量化的新方法.该方法充分利用了各高频子带系数频率相关性和空间约束性,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.对重要类矢量实行加权网格编码矢量量化,利用卷积编码扩展信号空间以增大量化信号间的欧氏距离,用维特比算法搜索最优量化序列,比使用矢量量化提高了0.6db左右.该方法编码计算复杂度适中,解码简单,可达到很好的压缩效果.     

利用二维非分离型嵌入式子波系数的零树图象编码

本文讨论利用二维非分离型滤渡器组的嵌入式零树子波(Embedded Warder Zerotree，简称EWZ)图象编码。文中讨论了二维非分离型滤波器组完全重构的充要条件，还讨论了子带图象的分割和五点式抽样的问题。实验表明嵌入式零树编码方法可以用在非分离型二维子波变换情况，在低比特率时有很好的巅值信噪比PSNR。     

结合相干斑抑制的小波域SAR图像网格编码量化

本文结合SAR图像相干斑的抑制,研究了一种在小波域对带噪SAR图像做网格编码量化的新方法。首先将SAR图像在小波域内实施软阀值去噪声,然后根据SAR图像在小波域中各子带系数固有的树结构关系对其进行零树分类,对分类后的重要性小波系数进行网格编码量化,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,并用维特比算法寻找最优量化序列。该方法综合了小波相干斑抑制、零树编码、网格编码量化技术,不仅利用了信号小波变换域的空间相关性,而且也较好地利用了信号间的时间相关性。在压缩的同时进行了相干斑抑制,在SAR图像的压缩中取得了很好的效果。     

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。     

可变树长混合小波和子树自量化分形视频编码

对可变树长混合小波和子树自量化分形视频编码方案进行了探讨。通过金字塔小波分解，每一频序列帧被分解为多频率子带，将它们按一定方式组织成小波子树结构来表示视频序列的运动特性。对这些小波子树者运动检测，分成运动树和非运动树两类。非运动树的编码直接、简单；运动树则采用可变树长混合小波和对自量化方法来进行编码。实验结果表明在低比特率情况下，所述的方案在ＰＳＮＲ和主观质量两方面均可获得较好的性能。     

一种二维离散子波变换的滤波器结构

子波变换具有良好的时间（空间）频率局部化性能，在图象子带编码中二维离散子波变换是一种接近理想的子带分析／综合子系统．本文提出一种利用一维离散子波变换实现二维有限长离散子波变换的方法，同时给出了二维离散子波正变换（DWT）和反变换（IDWT）的滤波器实现结构．实验结果表明新的方法具有良好的重构性，完全适用于图象压缩编码系统中的分析／综合子系统．     

基于有限状态格点向量量化器的子波图像压缩编码技术

子波图像的各子带间通常存在着很强的能量相关性，而有限状态量化模型可以用来很好地描述自然图像子波域内不同标度间的自相似性。向量量化较之标量量化往往具有更好的率／失真特性及更高的编码效率，但传统基于LBG算法设计的向量码书没有固定的结构，其近乎“随机”的码书分布造成设计和编码时极大的计算负担；空间R^N中的格点具有清晰的结构，基于格点的向量量化器在基本不影响量化性能的前提下提供了在设计／量化时极大的计算简化。本文提出了一种基于格点向量量化器的子波图像编码算法，它可被视作以上两种技术的有机结合，它既能有效的描述子波图像间的能量相关性，同时也带来了极大的计算简化。子波图像有限状态格点向量量化器的设计是基于一种新的自适应量化策略，它很好地描述了格点量化器率／失真特性，并能得到很好的编码结果。     

Hierarchical partition priority wavelet image compression

Image compression methods for progressive transmission using optimal hierarchical decomposition, partition priority coding (PPC), and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, a hierarchical subband/wavelet decomposition transforms the original image. The analysis filter banks are selected to maximize the reproduction fidelity in each stage of progressive image transmission. An efficient triple-state differential pulse code modulation (DPCM) method is applied to the smoothed subband coefficients, and the corresponding prediction error is Lloyd-Max quantized. Such a quantizer is also designed to fit the characteristics of the detail transform coefficients in each subband, which are then coded using novel hierarchical PPC (HPPC) and predictive HPPC (PHPPC) algorithms. More specifically, given a suitable partitioning of their absolute range, the quantized detail coefficients are ordered based on both their decomposition level and partition and then are coded along with the corresponding address map. Space filling scanning further reduces the coding cost by providing a highly spatially correlated address map of the coefficients in each PPC partition. Finally, adaptive MDEC is applied to both the DPCM and HPPC/PHPPC outputs by considering a division of the source (quantized coefficients) into multiple subsources and adaptive arithmetic coding based on their corresponding histograms. Experimental results demonstrate the great performance of the proposed compression methods.     

整数小波框架下基于阈值分割的静止图像编码

首先,选定分割阈值Th。然后,对大于Th的小波系数采用整数平方阈值进行量化、缩短阈值间的距离,并对阈值平面上的系数采用改进的二进制SPECK编码框架,对小于Th的系数采用2的整数幂作为量化阈值。最后,在每个阈值平面上均采用无乘法的二进制算术编码。通过与嵌入式零树小波(EZW)、SPIHT及SPECK算法的实验结果比较,ETSC算法不仅有较低的计算复杂度,而且提高了整数小波变换(IWT)在低比特率下的编码效率。此外,该算法支持有损和无损解码在单一码流下完成。     

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.     

Real-time implementation of a new low-memory SPIHT image coding algorithm using DSP chip

Among all algorithms based on wavelet transform and zerotree quantization, Said and Pearlman's (1996) set partitioning in hierarchical trees (SPIHT) algorithm is well-known for its simplicity and efficiency. This paper deals with the real-time implementation of SPIHT algorithm using DSP chip. In order to facilitate the implementation and improve the codec's performance, some relative issues are thoroughly discussed, such as the optimization of program structure to speed up the wavelet decomposition. SPIHT's high memory requirement is a major drawback for hardware implementation. In this paper, we modify the original SPIHT algorithm by presenting two new concepts-number of error bits and absolute zerotree. Consequently, the memory cost is significantly reduced. We also introduce a new method to control the coding process by number of error bits. Our experimental results show that the implementation meets common requirement of real-time video coding and is proven to be a practical and efficient DSP solution.     

一种新颖的矩形块-零树编码方法

提出了一种新颖的矩形块-零树编码方法,它采用零树编码方法结合了矩形块填充编码的思想对图像数据进行压缩编码。利用图像小波系数在幅值及相关性上具有的特点,对第3层小波系数采用矩形块填充法原则来组合一系列粗壮的树根,并由此延伸成一棵棵矩形块-零树,然后参照零树法对矩形块-零树处理,剩下的小波系数则按照零树法处理。实验证明,它是一种具有良好性能和较小计算量的图像压缩编码方法。     

Scalable intraband wavelet‐based coding scheme for irregular meshes

This paper describes a novel scalable 3D triangular mesh coding method based on wavelet transform and successive approximation quantization. The algorithm efficiently exploits the intracorrelations between wavelet coefficients independently in each subband. Non‐significant wavelet coefficients are clustered, in a per bit‐plane manner, by using an octree‐based approach. An hierarchical bitstream is then generated allowing to gradually decode the 3D mesh at the desired quality or resolution representation. Our proposal can be executed on arbitrary topology meshes by using irregular wavelet decomposition. Objective and subjective quality evaluation on representative 3D meshes shows that the proposed codec provides competitive compression results when compared to the state‐of‐the‐art. Furthermore, it fits well to applications that require fast interactive handling of highly detailed 3D meshes, over networks with limited and/or variable bandwidth.     

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.