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.     

VLSI Implementation of Discrete Wavelet Transform for Lossless Compression of Medical Images

This paper presents a VLSI architecture to implement the forward and inverse two dimensional Discrete Wavelet Transform (DWT), to compress medical images for storage and retrieval. Lossless compression is usually required in the medical image field. The word length required for lossless compression makes too expensive the area cost of the architectures that appear in the literature. Thus, there is a clear need for designing a cost-effective architecture to implement the lossless compression of medical images using DWT. The data path word length has been selected to ensure the lossless accuracy criteria leading a high speed implementation with small chip area. The pyramid algorithm is reorganized and the algorithm locality is improved in order to obtain an efficient hardware implementation. The result is a pipelined architecture that supports single chip implementation in VLSI technology. The implementation employs only one multiplier and 352 memory elements to compute all scales what results in a considerable smaller chip area (45 mm²) than former implementations. The hardware design has been captured by means of the VHDL language and simulated on data taken from random images. Implemented in a 0.7 μm technology, it can compute both the forward and inverse DWT at a rate of 3.5 512×512 12 bit images/s corresponding to a clock speed of 33 MHz. This chip is the core of a PCI board that will speedup the DWT computation on desktop computers.     

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。     

面向星载计算机的双重索引数据压缩方法

随着星载计算机系统功能的日益复杂,程序规模也在快速扩大.在存储资源极其受限的背景下,需要稳定、有效的代码压缩功能来保障星载软件的正常存储与运行.混合压缩算法是目前无损数据压缩的主流算法,具有压缩率高、代码规模和计算资源需求大的特点.然而,在航天星载计算机等嵌入式系统中,由于其运行环境特殊,需要较高的可靠性和抗干扰能力,无法实现混合压缩算法应有的效果.同时,单一压缩模型压缩率较低.针对以上问题,在LZ77算法代码体积和内存消耗优势的基础上提出了改进方法:为压缩过程设计一种新的匹配记录表以存储高价值数据索引来辅助压缩,实现了原算法局部性优势与高价值数据全局分布的互补,更大程度上减少了数据冗余;结合动态填充、变长编码等进一步优化编码结构,降低存储需求;最终,设计并实现了一种更加适合航天嵌入式环境的无损数据压缩算法(LZRC).实验结果表明:(1)新算法在比LZ77算法代码体积仅多出3.5 KB的条件下,对软件代码的平均压缩比提高了17%;(2)新算法的运行内存需求仅为混合压缩算法的12%,代码体积也减少了84%,更加适合星载计算机系统.     

基于整数小波和三维自适应的高光谱图像无损压缩算法

考虑到高光谱图像小波子图的谱间相关性,提出了一种新的基于整数小波的三维自适应预测高光谱图像无损压缩算法。首先用5/3整数小波将高光谱每个谱段图像做小波分解,对不同谱段的相同子带,设计一种新的线性预测器。用与待预测像素有较强相关性的相邻像素自适应地估计预测系数的值。消除了大部分的谱间冗余和空间冗余后,再用JPEG-LS进一步去除残差图像的空间冗余。实验表明,该算法能有效去除多光谱图像间的相关性,较其他压缩算法压缩比有很大提高,且算法简单,便于硬件实现。     

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。     

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。     

一种基于网格编码量化的高光谱图像无损压缩方法

由于遥感图像的数据量非常庞大,给有限的存储空间和传输带宽带来很大的压力,同时,由于高光谱图像获取代价昂贵,具有广泛的应用领域,且压缩时一般不能丢失任何信息,即要求无损压缩,因此没有有效的压缩方法,高光谱图像的普及应用将受到极大的限制.网格编码量化（TCQ）借鉴了网格编码调制（TCM）中信号集合扩展、信号集合划分和网格状态转移的思想,其虽具有良好的均方误差（MSE）性能,而且计算复杂度适中,但目前TCQ主要被应用于图像的有损压缩,为了对高光谱图像进行有效的无损压缩,通过将TCQ引入高光谱图像的无损压缩,并根据高光谱图像的特点,提出了一种基于小波变换和TCQ的高光谱图像无损压缩方法.实验结果表明,与JPEG2000和JPEG-LS中无损压缩算法相比,该算法对高光谱图像具有更好的压缩性能.     

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.     

Development and investigation of a hierarchical compression algorithm for storing hyperspectral images

The characteristics of SpecTIR and AVIRIS 16-bit hyperspectral images are analyzed. The requirements for compression of such images are formulated. The aspects of using the hierarchical compression algorithm in hyperspectral images storage are studied. Spectral component approximation algorithms are considered that allow both an increased compression ratio and retrieval of particular components. Interpolation algorithms are considered and a rank interpolator is offered for hyperspectral images compression. Real 16-bit hyperspectral images are used in computational experiments to investigate the efficiency of the proposed algorithms. The best parameters of these algorithms are found experimentally and general recommendations on how to tune the proposed hierarchical compression algorithm to suit hyperspectral images storage problems are given.     

基于整数小波变换的遥感超光谱图像无损压缩

由于遥感图像信息十分宝贵,应尽可能采用无损压缩或近无损压缩方法。本文提出基于"提升"(Lifting)算法实现整数小波变换(IWT)的方法,将变换方法用于去除空间冗余,保证了小波变换后图像信息无损失,再用算术编码对图像进行无损压缩。由于变换方法的去相关性能良好,实验证明将IWT应用于遥感图像无损压缩是有效的,能达到预期的目的,解码后的图像能无失真的恢复。     

基于KLT和HEVC的嵌入式高光谱图像实时压缩

现有追求高压缩质量的高光谱图像压缩算法普遍存在计算复杂度高、离线式处理、嵌入式平台实现难度大等问题,目前很难得到实际应用。为解决以上问题,设计一种基于KLT和HEVC的嵌入式高光谱图像实时压缩方法。首先基于KLT去除谱间相关性,然后基于HEVC去除空间相关性并完成量化编码的过程。基于NVIDIA Jetson TX1平台,设计并实现了CPU和GPU异构并行压缩处理系统。利用真实数据集对所设计算法和所实现平台进行了性能及可行性验证。实验结果表明:在相同压缩比下,与离散小波变换（DWT）+JPEG2000算法相比,该系统明显提升了重建精度,在峰值信噪比（PSNR）方面平均提高了1.36 dB;同时,相比CPU,在GPU中进行KLT计算也至多可缩短33%的运行时间。     

Distributed near lossless compression algorithm for hyperspectral images

This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights the energy of each block under the target rate constraints is introduced. A simple linear prediction model is employed to construct the side information of each block for Slepian–Wolf coding. The relationship between the quantized step size and the allocated rate of each block is determined under the condition of correct reconstruction with the side information at the Slepian–Wolf decoder. Slepian–Wolf coding is performed on the quantized version of each block. Experimental results show that the performance of the proposed algorithm is competitive with that of state-of-the-art compression algorithms, making it appropriate for on-board compression.     

基于可逆整数DCT的遥感图像的无损压缩

通过程序设计实现了基于整数DCT的无损压缩算法，适用于遥感图像压缩。该算法对遥感图像进行整数DCT无损变换，用算术编码对变换系数进行了压缩。测试结果证明，相比于现有的基于DCT的无损压缩算法，压缩率有了一定的降低，对于遥感图像的压缩效果要好于普通无损压缩算法，可以在同一个系统中同时实现图像的无损/有损压缩，有一定的实用价值。     

FPGA-based architecture for hardware compression/decompression of wide format images

In this article, we present a popular lossless compression/decompression algorithm, GZIP, and the study to implement it on an FPGA-based architecture, the ADM-XRC board from ALPHA DATA parallel system ltd. The algorithm is lossless, and applied to “bi-level” images of large size (A0 format). It ensures a minimum compression rate for the images we are considering. It aims to decrease storage requirements and transfer times, which are critical for wide format printing systems. In a wide format document industry, raster data are most of time processed in an uncompressed format, in order to apply processing (P) before printing (p). An example of a copy chain is composed of scanner, set of processing operations, storage, link and printer. We propose to use a compressed format as the new data-flow representation to improve the performances of the printing system. For example, the compression (C) is applied as soon as the data are produced by the scanner, and decompression (D) is performed at the last stage, before printing. The set of processing is applied to compressed images. The proposed architecture for the compressor is based on a hash table and the decompressor is based on a parallel decoder of the Huffman codes. We implemented the proposed architecture for compression and decompression algorithms on FPGA Xilinx Virtex XCV 400.     

一种遥测数据实时压缩系统

介绍了一种遥测数据实时压缩系统的设计方案,该设计以FPGA+DSP为硬件架构,以具有一阶差分预测的ARC编码为无损压缩方案,达到了较高的压缩去除率和较快的压缩速度,适合在可靠性要求较高的遥测系统中使用。经实验验证,无损压缩系统有效地缓解了遥测系统的传输带宽压力。     

整数(5,3)小波变换结合SPIHT的无损图像压缩

讨论了整数(5,3)小波变换的构造方法,在此基础上提出了一种整数(5,3)小波与SPIH T以及自适应编码相结合的无损图像压缩编码方法,该方法运算速度快,硬件实现简单,实验结果表明,与其他的无损压缩方法相比,压缩比均有提高,是一种有效的无损图像压缩方法。     

A multidisciplinary user acceptability study of hyperspectral data compressed using an on‐board near lossless vector quantization algorithm

To deal with the extremely high data rate and huge data volume generated on‐board a hyperspectral satellite, the Canadian Space Agency (CSA) has developed two fast on‐board data compression techniques for hyperspectral imagery. The CSA is planning to place a data compressor on‐board a proposed Canadian hyperspectral satellite using these techniques to reduce the requirement for on‐board storage and provide a better match to available downlink capacity. Since the compression techniques are lossy, it is essential to assess the usability of the compressed data and the impact on remote sensing applications. In this paper, 11 hyperspectral data users covering a wide range of application areas and a variety of hyperspectral sensors assessed the usability of the compressed data using their well understood datasets and predefined evaluation criteria. Double blind testing was adopted to eliminate bias in the evaluation. Four users had ground truth available. They qualitatively and quantitatively compared the products derived from the compressed data to the ground truth at compression ratios from 10?:?1 to 50?:?1 to examine whether the compressed data provided the same amount of information as the original for their applications. They accepted all the compressed data. The users who did not have ground truths available evaluated the compression impact by comparing the products derived from the compressed data with those derived from the original data. They accepted most of the compressed data.     

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。     

高光谱图像非线性解混方法的研究进展

由于空间分辨率的限制,高光谱遥感图像中存在大量混合像元,对混合像元的解混是实现地物精确分类和识别的前提。与传统的线性解混方法相比,非线性解混方法在寻找组成混合像元的端元以及每个端元的丰度时具有较高的精度。分析了光谱非线性混合的原理,总结了近年来提出的非线性解混算法,重点对双线性模型、神经网络、基于核函数的非线性解混算法以及基于流形学习的非线性解混算法进行了介绍和分析。最后总结了混合像元非线性解混未来发展的趋势。