An Approach to Integer Wavelet Transform for Medical Image Compression in PACS

We study an approach to integer wavelet transform for lossless compression of medical image in medical picture archiving and communication system (PACS). By lifting scheme a reversible integer wavelet transform is generated, which has the similar features with the corresponding biorthogonal wavelet transform. Experimental results of the method based on integer wavelet transform are given to show better performance and great applicable potentiality in medical image compression.

Medical image lossless compression based on combining an integer wavelet transform with DPCM

To improve the classical lossless compression of low efficiency,a method of image lossless compression with high efficiency is presented.Its theory and the algorithm implementation are introduced.The basic approach of medical image lossless compression is then briefly described.After analyzing and implementing differential plus code modulation(DPCM)in lossless compression,a new method of combining an integer wavelet transform with DPCM to compress medical images is discussed.The analysis and simulation results show that this new method is simpler and useful.Moreover,it has high compression ratio in medical image lossless compression.

Seismic data compression based on integer wavelet transform

Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases. In the paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparing with the traditional algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet family can lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its capability in can- celing data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function of the lossless compression seismic data.

Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this study.First of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public keys.To make the chaotic sequence more random,a mathematical model is constructed to improve the random performance.Then,the plain image is compressed and encrypted to obtain the secret image.Secondly,the secret image is inserted with numbers zero to extend its size same to the plain image.After applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier image.Finally,a meaningful carrier image embedded with secret plain image can be obtained by inverse IWT.Here,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic system.Especially,information entropy of the plain image is employed to produce the initial conditions of chaotic system.As a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the communication.Experimental simulations show that the normalized correlation(NC)values between the host image and the cipher image are high.That is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

Second Generation Wavelet Applied to Lossless Compression Coding of Image

In this paper, the second generation wavelet transform is applied to image lossless coding, according to its characteristic of reversible integer wavelet transform. The second generation wavelet transform can provide higher compression ratio than Huffman coding while it reconstructs image without loss compared with the first generation wavelet transform. The experimental results show that the se cond generation wavelet transform can obtain excellent performance in medical image compression coding.

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.

Integer Wavelet-Based Image Interpolation in Lifting Structure for Image Resolution Enhancement

A floating-point wavelet-based and an integer wavelet-based image interpolations in lifting structures and polynomial curve fitting for image resolution enhancement are proposed in this paper. The proposed prediction methods estimate high-frequency wavelet coefficients of the original image based on the available low-frequency wavelet coefficients, so that the original image can be reconstructed by using the proposed prediction method. To further improve the reconstruction performance, we use polynomial curve fitting to build relationships between actual high-frequency wavelet coefficients and estimated high-frequency wavelet coefficients. Results of the proposed prediction algorithm for different wavelet transforms are compared to show the proposed prediction algorithm outperforms other methods.

基于谱间DPCM和整数小波变换的超光谱图像无损压缩

分析了干涉成像光谱仪所获取图像的谱间和空间相关性,提出了一种混合无损压缩方案.首先进行谱间DPCM预测,再对残差图像采用整数小波变换,最后对小波系数进行二值自适应算术编码.实验结果表明,该算法可实现无损压缩,压缩比平均可达2.018,较二维整数小波变换算法提高40.3%.并且算法复杂度较低,只有加减和移位运算,易于硬件实现.

DPCM与小波变换结合的医学图像无损压缩

为了解决经典无损压缩方法效率较低的问题,提出了改善医学图像无损压缩效率的方法,并对此方法的原理和算法实现作了详细的介绍和分析.简述了医学图像无损压缩的基本步骤,对差分脉冲编码调制(DPCM)在无损压缩中的作用进行了分析和实现,并在此基础上,提出将整数小波变换与DPCM相结合的医学图像无损压缩方法.通过对比和分析实验结果可知,采用该方法进行的医学图像无损压缩具有算法简洁、压缩比高,实用性强等特点.

DPCM与整数小波变换相结合的图像无损压缩

论文讨论了将DPCM变换与整数小波变换相结合的方法来实现图像的无失真压缩。在论文压缩算法中,首先对图像进行DPCM预测,将差值图像经过整数小波变换,然后再用无损SPIHT算法进行压缩编码,最后再经过相应的逆变换即可以得到重构的无失真图像。该方法简单易懂,硬件实现方便。仿真结果表明,这是一种效果很好的图像无损压缩方法。

用整数Haar小波变换和分块DPCM实现静止图像数据的信息熵保持型压缩编码

本文首先讨论了整数Haar小波的构造方法 ,然后利用分块DPCM与整数Haar小波变换进行信息保持图像数据压缩 ,该方法可进行实时处理 ,硬件实现简单 ,可并行处理 ,实验结果表明 。

DPCM与整数小波变换结合的医学图像无损压缩算法

提出了一种新的用于医学图像无损压缩的算法DPCM IWT。该算法可进行实时处理,硬件实现简单。实验结果表明,DPCM IWT算法的压缩率分别比IWT算法和DPCM方法平均提高12%和13.6%左右。

整数Haar变换结合DPCM的无失真图像压缩

讨论了整数Ｈａａｒ 小波的构造方法，在此基础上提出了一种整数Ｈａａｒ 小波与ＤＰＣＭ 相结合的无失真图像编码方法，该方法运算速度快，硬件实现简单，与ＡＲＪ和ＪＰＥＧ 中使用的无失真编码方法相比，压缩比平均提高了１２ ％

DPCM与EZW混合编码在医学图像中的应用研究

现代医疗成像设备产生了大量高价值的医学图像,如何对信息进行有效的存储、查询以及网络传输是一个亟待解决的问题。利用整数小波变换和零树编码算法对医学图像进行了压缩研究,在对嵌入零树小波编码(Embedded Zero-tree Wavelet,EZW)算法研究的基础上提出一种基于小波系数的嵌人零树小波编码算法。其主要包括:对小波图像的低频子带(LL)进行单独DPCM编码;对高频子带改变扫描方式以获得最佳编码排序,最后用游程编码(RLE)实现输出。实验结果表明,该方法是一种比较有效的编码方法。

基于DPCM的渐进图像压缩和编码格式

提出了一种基于DPCM的从有损到无损的渐进图像压缩和编码格式。算法分为两个阶段。第一个阶段采用基于马尔可夫模型预测的DPCM算法得到低分辨率的近似图像;第二个阶段对量化误差进行渐进传输,使图像被渐进地完善。它通过对量化误差实施小波变换,根据量化误差的局部频率特性,用简单的规则把小波变换后的系数进行分组,最终使得渐进图像传输成为可能。实验结果表明,由于使用了马尔可夫模型和小波变换,可以更快的得到细节丰富的图像。

基于整数小波变换和DPCM的混合图像编码(英文)

通过小波变换进行图像压缩 ,并在DPCM /DCT的基础上开发了一种DPCM ?WT编码算法系统 以目标驱动和数据流驱动为依据 ,提出一种DPCM /WT的新编码的算法系统 该算法系统在图像压缩比例、保真性、实时处理上都比DPCM

基于整数小波变换和DPCM的医学图像近无损压缩

医学图像压缩方法在近十年中得到广泛的研究,在当今医疗技术飞速发展的迫切要求下,国内外正不断探索更新更有效的医学图像压缩方法.近无损压缩方法由于其灵活的误差控制和高效的压缩性能,成为医学研究领域中的重要课题.本文研究了小波技术在医学图像近无损压缩中的应用,采用整数小波变换结合DPCM方法进行近无损压缩.实验结果表明,该方法与传统近无损压缩方法相比,不仅具有高压缩率,而且具有更好的编码传输特性.

基于整数小波变换的多光谱图像无损压缩

多光谱图像一般都采用预测方法去除空间冗余和谱内冗余实现无损压缩。通过用提升方法构造整数小波变换 ,将变换方法用于去除空间冗余 ;通过分类方法构造谱间预测器 ,用预测方法去除谱间冗余 ,两者相结合 ,实现无损压缩。由于变换方法的去相关性能良好 ,使该方法压缩效果大大改善。

一种适合星上应用的遥感图像有损压缩算法

提出了一种高质量的遥感图像有损压缩算法,在分析了遥感图像经Daubechies双正交小波基D5/3整数小波变换后各子带小波系数统计特性和能量分布的基础上,引入了人类视觉特性,用它来控制算法中的量化方案。根据能量的大小确定不同子带对于目标识别的重要程度,选择不同的量化阈值和量化步长进行量化,并对量化后的数据采用固定比特平面编码。仿真实验表明,该算法对于不同内容和纹理的图像,在一定的压缩比下,均获得了PSNR(峰值信噪比)>30dB的恢复图像,在不损失最低频信息的同时较好地保持了遥感图像中丰富的高频信息,实现了高质量的图像压缩,并且算法简便,快捷,所占用的存储容量小,易于硬件实现,适合于星上应用,减少了在遥感图像压缩中小目标的丢失。

基于整数小波变换的地震数据压缩

由于地震数据的特殊性 ,在某些情况下需要进行无损压缩 .本文通过应用提升方法构造的整数小波变换无损压缩地震数据 ,与传统的无损压缩算法相比 ,可以较好地提高压缩率 .CDF(2 ,n)系列双正交小波可以较好地消除数据间冗余 ,集中数据特征 ,比其它CDF系列、SWE和CRF双正交小波有更好的压缩效果 .CDF(2 ,n)系列双正交小波更适合作为整数小波变换无损压缩地震数据的小波函数 .