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.     

Hybrid Fractal-Wavelet Method for Multi-Channel EEG Signal Compression

In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods.     

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.     

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。     

嵌入式小波编码算法的分类研究

小波编码技术关键在于如何有效地组织和量化图像的小波变换系数，如何提高图像的压缩效率和图像的恢复质量。将较经典的小波编码方法按照小波系数的不同相关性和不同的小波变换处理进行了分类研究，分析了它们的原理和性能。最后提出了小波图像编码方法的进一步研究方向。     

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.     

一种基于小波系数重要图编码的图像压缩算法

提出了一种新的基于小波系数重要图编码的图像压缩算法。该算法根据量化后的波系数的特点进行了一种期望排序，然后舍掉序列后面大量的零值小波系数，从而得到一个波系数子集，能以少的小波系数来很好地逼近原始图像，省去了零树编码中零树结构带来的大量比特开销。实验表明，该算法与MPEG-4的静止图像压缩算法相比较，重构图像的峰值信噪比（PSNR）值在相同码率下有较大的提高。     

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).     

Statistical modelling of the wavelet coefficients with different bases and decomposition levels

Various wavelet coefficient statistics are useful to increase the compression efficiency of images. The distribution of the wavelet coefficients within a sub-band affects how the decompression values should be adjusted. A generalised Gaussian distribution model is seen to be applicable from a theoretical point of view, and a convenient way is provided to estimate the parameters from the empirical data. This knowledge is applied to examine the best statistical modelling of the wavelet coefficients for different wavelet bases and decomposition levels.     

A lossless compression algorithm of remote sensing image for space applications

A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio （CR） of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.     

Wavelet and wavelet packet compression of phonocardiograms

The first reported method intended for the compression of the phonocardiogram, using wavelet coefficients thresholding, together with Huffman and run length encoding coding techniques to obtain further compression is described. Results show better compression rates and lower distortion than standard audio compression techniques.     

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.     

多层离散小波变换系数的稀疏向量构造的改进

融合离散小波变换和压缩感知的图像压缩方案很好避免了采用离散余弦变换和压缩感知时所带来的块效应,但当前基于单层离散小波变换的算法压缩比较低,基于多层离散小波变换的算法重构质量不佳。为了解决这些不足,根据离散小波变换系数的特点,对现有基于多层离散小波变换的算法提出了改进。图像经小波变换后,保留图像最高层低频系数,高频系数的构造方式给予适当改进。实验结果表明,与现有算法相比,重构图像的PSNR值得到2～4 dB提高。     

An efficient coding algorithm for the compression of ECG signals using the wavelet transform

A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.     

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.     

小波模极大值点的信号稀疏表示及重建

作为压缩感知理论的前提,稀疏表示要求信号本身是稀疏的或者在某种正交基下可以稀疏表示。本文针对信号本身及小波变换后均不够稀疏的情况,提出一种基于模极大值点的信号稀疏表示算法。该算法在小波变换的基础上,利用小波分解的结构,对各层高频小波系数通过寻找其模极大值点的方法进行稀疏化,然后通过测量矩阵得到它的测量值,对测量点数进行熵编码以实现数据压缩传输。解码时,采用正交匹配追踪算法得到模极大值点的估计值,最后通过交替投影法重构出原信号。仿真结果表明,与经典压缩感知算法相比,该算法恢复信号的质量有较大提高,且由于稀疏度增大,所以信号具有更好的可压缩性,实验表明本文算法对复杂信号效果更明显。      

基于能量树编码的小波图像压缩算法

针对小波变换的空间能量聚集特性,提出了一种基于能量树编码的小波图像压缩算法。该算法在离散小波变换的基础上,分别对图像的各高频子带按其局部能量构建分层能量树,利用总能量和各层的能量角等效表示子带的小波系数;根据给定的压缩比,选择合适的代价函数构建最佳能量树,然后对其进行量化和编码,通过自适应的比特率分配实现小波图像压缩。实验结果表明,该算法实现简单,重构图像质量好,与当前多种主流的小波图像压缩算法相比,压缩性能有了明显提高。     

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.     

一种基于JPEG2000标准的数字图像加密算法

针对最新的图像压缩标准JPEG2000,提出了一种在JPEG2000压缩过程中实时对小波系数进行加密的方法.在具体算法上采用本文提出的小波置乱(简称为OCWF)方法并结合符号加密来实现对小波系数的实时加密.实验结果表明,该方法具有加密效果好、加密开销少、对压缩性能影响小等优点,且完全适合高压缩率的有损压缩,可以满足版权保护等应用场合的需要.     

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...