基于分类KLT的高光谱图像压缩

高光谱图像的有效压缩已经成为高光谱遥感领域研究的热点。提出了一种基于分类KLT（ Karhunen-Loeve Transform）的高光谱图像压缩算法。该算法利用光谱信息对高光谱图像进行地物分类,根据相邻波段的相关性对高光谱图像进行波段分组。在地物分类与波段分组的基础上,对每组的每一类地物数据分别进行KL变换,利用EBCOT（Embedded Block Coding with Optimal Trtmcation）算法对所有主成分进行联合编码。实验结果表明,该算法能够取得优于JPEG2000以及DWT-JPEG2000的压缩性能,适合实现高光谱图像的有效压缩。     

Near lossless compression of hyperspectral images based on distributed source coding

Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg.In order to solve the effective compression of on-board hyperspectral images,a new distributed near lossless compression algorithm based on multilevel coset codes is proposed.Due to the diverse importance of each band,a new adaptive rate allocation algorithm is proposed,which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints.Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder,which minimizes the distortion of reconstructed hyperspectral images under the target rate.Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams.Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.     

超光谱图像的三维小波嵌入零块压缩编码

超光谱图像作为一种三维图像,其海量的数据导致在有限带宽信道上传输和存储非常困难,必须对它进行有效的压缩编码.提出了一种基于非对称三维小波变换(3D wavelet transform,简称3DWT)和三维集合块分裂的超光谱遥感图像压缩方法.因为大多数超光谱图像在各个方向上具有非对称的统计特性,所以利用非对称三维小波变换去除图像的谱间和空间冗余.与传统的对称三维小波变换相比,非对称的三维小波变换能够更有效地去除相邻谱段间的冗余.提出了一种改进的3DSPECK(3D set partitioning embedded block)算法--非对称三维集合分裂块算法(asymmetric transform 3DSPECK,简称AT-3DSPECK),并被用于编码变换后的系数.根据变换系数的能量分布特点,三维零块分裂和三维octave子带分裂方法被有效地结合在所提出的AT-3DSPECK算法中.为了优化率失真和加速编码速度,也给出了一种零块优化排序的快速算法.实验测试表明:AT-3DSPECK算法的平均PSNR(peak signal to noise ratio)分别比AT-3DSPIHT(asymmetric transform 3D set partitioning in hierarchical trees)和3DSPECK算法高0.4dB和1.4dB.此外,AT-3DSPECK还具有比零树算法更快的编码速度.     

采用谱间预测的高光谱图像压缩方法研究

高光谱图像具有较高谱分辨率的优越性是以其较大的数据量及较高的数据维为代价的,因此有必要研究有效的高光谱图像压缩方法。探讨一种基于谱间预测的高光谱图像压缩方案。考虑到高光谱图像谱间相关性随分辨率的提高而增强,推导出由多个波段对当前波段进行线性预测的预测器系数求解算法,提出了一种参考波段优化选取方法。实验结果表明,该方法能获得较低的最小均方误差,运算速度快,具有实用价值。     

Efficient compression algorithm for hyperspectral images based on correlation coefficients adaptive 3D zerotree coding

The authors propose an efficient compression algorithm for hyperspectral images, which is based on the correlation coefficients adaptive asymmetric tree three-dimensional (3D) set partitioning in hierarchical trees (AT-3DSPIHT) coding of the asymmetric 3D wavelet transform (A3D-DWT) coefficients. According to the characteristics of the correlation coefficients between adjacent spectral bands, a binary tree spectral band grouping algorithm is carried out to divide the adjacent spectral bands into different mode groups. Along with this, an appropriate A3D-DWT with the corresponding decomposition levels and a proper AT-3D zerotree are determined adaptively. Several airborne visible/infrared imaging spectrometer images are used to evaluate the proposed algorithm. Compared with the state-of-the-art 3D-DWT based algorithms, the proposed adaptive AT-3DSPIHT achieves the best compression performance at lower bit rates. Moreover, at the low correlated adjacent bands, the proposed algorithm also outperforms the 2DSPIHT algorithm.     

基于有向无环图的倒排链等字长划分压缩算法

在搜索引擎的倒排索引等字长（FWA）类型压缩算法中,倒排链的“贪心”分块划分策略和码字信息的交错存储使算法难以达到最优的压缩效果。针对上述问题,提出了一种基于有向无环图（DAG）的FWA划分压缩算法。首先,考虑到互联网网页聚类特性带来的倒排链小数字信息,设计了一种数据区为64位分块的新型FWA压缩格式。该压缩格式通过4位的指示区将数据区划分为16种适合于连续小数字压缩的存储模式,并将倒排链每个分块的指示位和数据位分类存储,从而保证了较好的批量解压性能。其次,在新压缩格式的基础上提出一种基于DAG描述的倒排链FWA划分压缩方法——固定字对齐划分（WAP）算法。该算法利用DAG将倒排链分块划分问题归结为单源最短路径（SSSP）问题,并考虑FWA压缩格式中数据区存储模式的限制条件来确定SSSP问题的结构形式和递归定义。然后,给出了采用动态规划求解SSSP问题并形成最优划分向量的伪码和算法复杂度,并对S9、S16、S8b等传统FWA算法的原有存储模式进行了基于DAG的划分优化,把优化前后的算法的计算复杂度进行比较分析。最后,使用仿真整数序列数据和文本检索会议（TREC） GOV2网页索引数据进行压缩性能实验。实验结果表明,相较于传统FWA类型算法,基于DAG的FWA划分算法在通过批量解压和划分优化技术提升算法的压缩率和解压速度同时,对连续小数字整数序列进行压缩时能够获得比传统参照框架（FOR）类型算法更高的压缩率。     

基于独立成分分析的高光谱图像有损压缩方法

提出一种结合小波变换和独立成分分析(ICA)的高光谱图像有损压缩方法。采用最大似然估计与最大噪声分离相结合的方法对原始高光谱数据进行维数估计。依据维数估计的结果在光谱方向上采用 ICA,在空间上运用离散小波变换。对于变换后的系数,使用多级树集合分裂算法和算术编码分别进行量化编码和熵编码。在机载可见光/红外成像光谱仪220波段高光谱数据上的实验结果表明,该算法可以在获得较高压缩率的同时,保留高光谱图像的光谱特性。     

基于64位体系结构的倒排索引压缩算法

在64位体系结构的CPU中,字长从32位扩展到64位,处理器每次可以处理的数据也增加到64位。这对搜索引擎使用的核心数据结构——倒排索引的压缩与解压缩带来一定的影响。针对当前32位整数字对齐压缩算法Simple不适用于64位系统的问题,对其进行改进,并提出3种基于64位的字对齐压缩算法,即SimpleX64-16、SimpleX64-32和SimpleX64-64。3种算法都采用多种压缩模式,并对每个模式进行压缩空间的优化。在64位机器上GOV2和ClueWeb09B数据集的倒排索引实验结果表明,与传统的基于32位字对齐的压缩算法相比,3种基于64位字对齐的算法在解压速度方面最多提高14.5%,在压缩率方面最多提高2.5%。     

基于降低映射预测残差的高光谱图像压缩算法

CCSDS 123.0-B-1算法是空间数据系统咨询委员会为多/高光谱图像提出的自适应三维预测无损压缩标准,针对CCSDS 123.0-B-1算法中存在的未充分利用像素位置信息及谱间相关性、压缩率有待提高的问题,对该算法的预测器进行了优化,提出了RMPR算法。RMPR算法根据当前像元具体位置对预测点进行自适应选择,采用双向线性预测去除高光谱图像的谱间相关性,并使用优化的残差映射器提高预测精度、缩短压缩码长。利用10幅高光谱图像进行测试,结果表明,在保证无损压缩且压缩效率无显著差异的前提下,RMPR算法的压缩性能显著优于原算法。     

High-dynamic-range still-image encoding in JPEG 2000

The raw size of a high-dynamic-range (HDR) image brings about problems in storage and transmission. Many bytes are wasted in data redundancy and perceptually unimportant information. To address this problem, researchers have proposed some preliminary algorithms to compress the data, like RGBE/XYZE, OpenEXR, LogLuv, and so on. HDR images can have a dynamic range of more than four orders of magnitude while conventional 8-bit images retain only two orders of magnitude of the dynamic range. This distinction between an HDR image and a conventional image leads to difficulties in using most existing image compressors. JPEG 2000 supports up to 16-bit integer data, so it can already provide image compression for most HDR images. In this article, we propose a JPEG 2000-based lossy image compression scheme for HDR images of all dynamic ranges. We show how to fit HDR encoding into a JPEG 2000 encoder to meet the HDR encoding requirement. To achieve the goal of minimum error in the logarithm domain, we map the logarithm of each pixel value into integer values and then send the results to a JPEG 2000 encoder. Our approach is basically a wavelet-based HDR still-image encoding method.     

Investigation into lossless hyperspectral image compression for satellite remote sensing

Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-infrared (IR), mid-IR and thermal IR portions of the spectrum, thus requiring large data storage on board the satellite and high bandwidth of the downlink transmission channel to ground stations. Image compression techniques are required to compensate for the limitations in terms of on-board storage and communication link bandwidth. In most remote-sensing applications, preservation of the original information is important and urges studies on lossless compression techniques for on-board implementation. This article first reviews hyperspectral spaceborne missions and compression techniques for hyperspectral images used on board satellites. The rest of the article investigates the suitability of the integer Karhunen–Loève transform (KLT) for lossless inter-band compression in spaceborne hyperspectral imaging payloads. Clustering and tiling strategies are employed to reduce the computational complexity of the algorithm. The integer KLT performance is evaluated through a comprehensive numerical experimentation using four airborne and four spaceborne hyperspectral datasets. In addition, an implementation of the integer KLT algorithm is ported to an embedded platform including a digital signal processor (DSP). The DSP performance results are reported and compared with the desktop implementation. The effects of clustering and tiling techniques on the compression ratio and latency are assessed for both desktop and the DSP implementation.     

Toward practical guideline for design of image compression algorithms for biomedical applications

Improvements in medicine and healthcare are accelerating. Information generation, sharing, and expert analysis, play a great role in improving medical sciences. Big data produced by medical procedures in hospitals, laboratories, and research centers needs storage and transmission. Data compression is a critical tool that reduces the burden of storage and transmission. Medical images, in particular, require special consideration in terms of storage and transmissions. Unlike many other types of big data, medical images require lossless storage. Special purpose compression algorithms and codecs could compress variety of such images with superior performance compared to the general purpose lossless algorithms. For the medical images, many lossless algorithms have been proposed so far. A compression algorithm comprises of different stages. Before designing a special purpose compression method we need to know how much each stage contributes to the overall compression performance so we could accordingly invest time and effort in designing different stages. In order to compare and evaluate these multi-stage compression techniques and to design more efficient compression methods for big data applications, in this paper the effectiveness of each of these compression stages on the total performance of the algorithm is analyzed.     

成像光谱图像压缩技术研究的新进展

随着成像光谱技术的迅速发展,成像光谱图像呈现出空间分辨率和光谱分辨率不断提高的特点,其呈几何级数增长的数据量与遥感平台上传输和存储的有限资源产生了巨大矛盾,这使得对海量数据进行有效的压缩成了遥感技术发展中迫切需要解决的一个问题.首先介绍了成像光谱图像的特点及其压缩框架,然后综述了成像光谱图像压缩算法的研究进展,最后对研究前景进行了展望.     

基于3维上下文预测的高光谱图像无损压缩

如今高光谱数据的有效压缩已成为遥感技术发展中需要迫切解决的问题,为了对高光谱数据进行有效压缩,提出了一种基于3维上下文预测的高光谱图像无损压缩算法。该算法首先根据相邻波段间的相关性大小进行波段分组,同时对各个分组重新进行波段排序;然后采用自适应波段选择算法对高光谱图像进行降维,再利用k-means算法对降维后的波段谱向矢量进行聚类;最后在参考波段和当前波段中通过定义3维上下文预测结构,在聚类结果的基础上,对各个分类分别训练其最优的预测系数。实验结果表明,该方法可显著降低压缩后图像编码的平均比特率。     

Application of detector precision characteristics and histogram packing for compression of biological fluorescence micrographs

Modern applications of biological microscopy such as high-content screening (HCS), 4D imaging, and multispectral imaging may involve collection of thousands of images in every experiment making efficient image-compression techniques necessary. Reversible compression algorithms, when used with biological micrographs, provide only a moderate compression ratio, while irreversible techniques obtain better ratios at the cost of removing some information from images and introducing artifacts. We construct a model of noise, which is a function of signal in the imaging system. In the next step insignificant intensity levels are discarded using intensity binning. The resultant images, characterized by sparse intensity histograms, are coded reversibly. We evaluate compression efficiency of combined reversible coding and intensity depth-reduction using single-channel 12-bit light micrographs of several subcellular structures. We apply local and global measures of intensity distribution to estimate maximum distortions introduced by the proposed algorithm. We demonstrate that the algorithm provides efficient compression and does not introduce significant changes to biological micrographs. The algorithm preserves information content of these images and therefore offers better fidelity than standard irreversible compression method JPEG2000.     

小波变换和SHA-1相结合的图像压缩加密

针对图像分层树集划分编码的安全性问题,提出一种将小波变换和SHA-1(security hash algorithm-1)相结合的图像加密算法(DSCE).首先将图像小波变换后的系数分为低频和高频两部分,然后将低频系数、初始密钥及图像像素和,通过SHA-1置乱加密SPIHT编码高频系数;将部分加密高频系数和初始密钥,通过SHA-1置乱加密Huff man编码低频系数,在压缩过程中实现低频和高频部分相互加密.仿真结果表明,DSCE算法密钥空间大,对密钥和明文敏感,同时有效提高图像存储和传输效率.     

高光谱图像压缩质量评价技术研究进展

众所周知,数据量的庞大,致使高光谱图像数据的应用受到很大限制。这种庞大的数据量对于许多情况,尤其对于卫星数据链路,由于受带宽和星上存储能力的局限,致使不能实时进行数据传输,因此必须使用有损压缩方式来减小高光谱图像的数据量。但由于有损压缩带来的信息丢失,对高光谱数据的不同后续应用影响不同,因此压缩图像的质量评价技术得到了广泛重视。为使人们对这一质量评价技术有一定了解,首先对高光谱图像压缩方法进行简单介绍;然后对现存的客观失真参数评价、应用算法统计结果评价、相似敏感度标准抽取评价等主要的压缩质量评价技术进行综述,同时比较它们的优缺点,并在此基础上,提出了一种基于最优性能的质量评估框架;最后对该技术今后的研究发展进行了展望。     

基于双通道变分自编码器的高光谱图像分类

针对现有高光谱图像变分自编码器（variational autoencoder,VAE）分类算法存在空间和光谱特征利用效率低的问题,提出一种基于双通道变分自编码器的高光谱图像深度学习分类算法。通过构建一维条件变分自编码器（conditional variational autoencoder,CVAE）特征提取框架和二维循环通道条件变分自编码（channel-recurrent conditional variational autoencoders,CRCVAE）特征提取框架分别提取高光谱图像的光谱特征和空间特征,将光谱特征向量和空间特征向量叠加形成空谱联合特征向量,将联合特征送入Softmax分类器中进行分类。在Indian pines和Pavia University两种高光谱数据集上进行了分析验证,实验结果显示,与其他算法相比,提出的算法在总分类精度、平均分类精度和Kappa系数等评价指标上至少提高了3.40、2.75和3.57个百分点,结果显示提出的算法得到了最高的分类精度和更好的可视化效果。     

基于重组的DPCM和位平面编码的高光谱图像压缩方案

高光谱图像压缩技术是遥感数据存储和传输中的一个迫切需要解决的问题。高光谱图像的特点是存在着两类冗余：空间冗余和谱间冗余。高光谱图像的压缩要同时利用图像的空间冗余和谱间冗余。基于重组的DPCM和位平面编码的压缩方法，是通过重组的DPCM，去除帧间相关性，消除超光谱图像帧间的冗余；然后对残差图像的压缩采用基于小波变换和位平面编码技术，去除空间冗余。实验取得了令人满意的效果，证明了该算法的有效性和实用性。     

基于增强空谱特征网络的空间感知协同表示算法

高光谱图像具有高维度、带间相关性较高、样本数量较少等诸多问题,直接利用表示学习算法对高光谱图像进行分类会导致严重的维数灾难.对于高光谱图像,不是所有的光谱带都可用于特定的分类任务.因此,文中提出基于增强空谱特征网络的空间感知协同表示算法.依据高光谱图像内在的低维流形构建基于空谱特征的分层网络.利用训练的网络对高维数据进...