小波变换和预测编码结合的抗差错SAR图像无损压缩算法

分析了即将推出的JPEG2000标准算法和基于自适应的上下文预测编码技术，提出了小波变换有损编码加自适应上下文预测无损残差编码的具有抗差能力的SAR图像无损压缩算法。该算法既具有小波变换图像编码的累进传输，对信道具有一定鲁棒性的特点，而且压缩率高于即将推出的国际标准JPEG2000的无损压缩和国际标准算法JPEG—LS。     

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。     

一种基于小波系数上下文模型的图像压缩方法

提出了二种新颖的基于小波系数上下文模型的图像压缩方法，该方法通过量化当前系数的线性预测值形成上下文．进行自适应的算术编码；同时利用了小波变换的多分辨率性质，以渐近分辨率的方式压缩图片，具有分辨率可扩展性。实验结果表明，该方法获得的无损压缩比高于SPIHT和用于JPEG2000的EBCOT，在各分辨率下的压缩比也高于EBCOT．压缩时间也比EBCOT要少。     

一种改进的图像自适应零树编码方法

在研究Shapiro零树图像编码方案的基础上,提出了一种改进型的自适应嵌入式零树编码方法.本算法利用自适应的小波系数,增加了编码过程中零树个数,提高了编码效率,使在相同压缩率情况下,提高PSNR约为0.1~0.6dB,同时该编码方案仍保持零树编码产生嵌入式码流、支持多码率解码的特点.     

一种改进的嵌入式零树小波图像编码算法

在分析嵌入式零树小波图像编码算法(EZW)的过程中,发现图像的小波系数按一定的顺序扫描,随着扫描的逐次进行,其零树根连续出现的概率越来越高.针对连续存在的零树根的现象,提出了一种改进的EZW算法,该算法采用6个标志位代替EZW算法中的4个标志位,对于子孙均为次要系数的重要系数,用新的标志位代替其子孙中大量连续出现的零树根,对6个标志位采用哈夫曼编码,再将编码后的标志位比特流与幅值量化得到的比特流组合进行游程编码.实验结果表明,在不影响其他标志位的前提下,重新分类重要系数极大的减少了零树根的数量,节省了扫描时间,提高了压缩比和编码效率.     

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).     

基于小波变换的预测四叉树图像编码

本文提出了一种图像编码的新算法,该算法利用了图像小波系数在频带内部与频带间的相关性,属于带内编码与带间编码的混合.在同一频带内将系数分块,随比特面的移动,将块由大到小进行四叉树分裂,以期最大限度的利用块内系数的相关性,克服了固定大小块的不足.同时在编码的过程中加入了预测过程,用上一比特平面的显著系数在当前比特面对其邻域和子节点系数进行预测,将上一比特平面的显著系数的邻域和子节点系数从块中取出单独编码,从而实现对块的裁剪,以使块的形状更符合实际的情况.最后熵编码采用的了基于上下文的算术编码,提出了四种上下文编码模型.通过对比实验表明,该方法的压缩性能较SPIHT、SQP、QT_L均有不同程度的提高.     

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.     

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。     

MODIS多光谱图像压缩研究

为有效存储MODIS多光谱图像数据,该文提出一种基于谱间预测和整数小波变换的多光谱图像压缩算法.首先通过构造谱间最优预测器去除谱间冗余,再利用整数小波变换和SPIHT算法对预测误差图像去除空间冗余,最后进行自适应算术编码.该方法可实现MODIS多光谱图像的无损、近无损和有损压缩,取得了满意的实验结果;在不同小波基条件下与3D-SPIHT算法比较,表明了该方法的有效性.     

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.     

Context modeling based on context quantization with application in wavelet image coding

Context modeling is widely used in image coding to improve the compression performance. However, with no special treatment, the expected compression gain will be cancelled by the model cost introduced by high order context models. Context quantization is an efficient method to deal with this problem. In this paper, we analyze the general context quantization problem in detail and show that context quantization is similar to a common vector quantization problem. If a suitable distortion measure is defined, the optimal context quantizer can be designed by a Lloyd style iterative algorithm. This context quantization strategy is applied to an embedded wavelet coding scheme in which the significance map symbols and sign symbols are directly coded by arithmetic coding with context models designed by the proposed quantization algorithm. Good coding performance is achieved.     

Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms

We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on context modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.     

Lossless image compression with projection-based and adaptive reversible integer wavelet transforms

Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard. In this paper, a projection-based technique is presented for decreasing the first-order entropy of transform coefficients and improving the lossless compression performance of reversible integer wavelet transforms. The projection technique is developed and used to predict a wavelet transform coefficient as a linear combination of other wavelet transform coefficients. It yields optimal fixed prediction steps for lifting-based wavelet transforms and unifies many wavelet-based lossless image compression results found in the literature. Additionally, the projection technique is used in an adaptive prediction scheme that varies the final prediction step of the lifting-based transform based on a modeling context. Compared to current fixed and adaptive lifting-based transforms, the projection technique produces improved reversible integer wavelet transforms with superior lossless compression performance. It also provides a generalized framework that explains and unifies many previous results in wavelet-based lossless image compression.     

基于多阈值与嵌入零树小波的图像压缩算法

本文提出了一种基于多阈值与嵌入零树小波的新图像压缩算法（简称为MEZW）,该算法能够通过以下3项措施提高EZW算法工作效率：（1）通过定义多阈值以完善逐次副近量化过程;（2）通过修改嵌入编码策略以消除编码冗余;（3）通过采纳关联模型以提高算术编码效率。     

Lossless image compression based on optimal prediction, adaptivelifting, and conditional arithmetic coding

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.     

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。     

小波包变换在水声信号压缩编码中的应用

应用小波包变换及Huffman编码技术相结合的方法对水声信号进行了压缩编码。先对水声信号进行小波包分解;然后对分解系数进行阈值处理,最后对阈值后的系数进行Huffman编码。使用了两种阈值方案,通过仿真比较．其中不同频段不同阈值方案的零率比全局阈值的零率高。尽管全局阈值的零率稍低一些,但其Huffman编码效率较高且硬件实现较为简单,因此最后选取全局阈值进行闽值量化。仿真结果表明,本算法对水声信号压缩编码效果理想。