Multispectral image compression using three-dimensional transform zeroblock coding

This paper proposes a new multispectral image data compression algorithm (KLT/WT-3DEZB). The proposed coding strategy consists of three main steps. Firstly, a wavelet transform (WT) is applied to reduce the spatial redundancies. Then, a Karhunen-Loeve transform (KLT) is used to reduce the redundancies in the spectral domain. Finally, a modified SPECK algorithm-three-dimensional embedded zeroblock (3DEZB) algorithm is proposed and used to encode the transformed coefficients. Numerical experiments show that the reconstructed images using the proposed algorithm exhibite a better quality and a higher compression ratio than those obtained by traditional KLT/WT-3DSPIHT, 3DSPIHT, and 3DSPECK algorithms.     

Multispectral Image Compression for Improvement of Colorimetric and Spectral Reproducibility by Nonlinear Spectral Transform

The article proposes a multispectral image compression scheme using nonlinear spectral transform for better colorimetric and spectral reproducibility. In the method, we show the reduction of colorimetric error under a defined viewing illuminant and also that spectral accuracy can be improved simultaneously using a nonlinear spectral transform called Labplus, which takes into account the nonlinearity of human color vision. Moreover, we show that the addition of diagonal matrices to Labplus can further preserve the spectral accuracy and has a generalized effect of improving the colorimetric accuracy under other viewing illuminants than the defined one. Finally, we discuss the usage of the first-order Markov model to form the analysis vectors for the higher order channels in Labplus to reduce the computational complexity. We implement a multispectral image compression system that integrates Labplus with JPEG2000 for high colorimetric and spectral reproducibility. Experimental results for a 16-band multispectral image show the effectiveness of the proposed scheme.     

基于直方图变换的多光谱图像3D SPIHT压缩编码算法

提出了新的多光谱图像压缩方案,直方图变换的三维分层树的集划分(3D SPIHT)压缩编码算法。基于多光谱图像的成像特点,在去相关之前,提出一种可逆的直方图变换方法对多光谱各波段图像灰度值进行调整,来提高各波段间的相关性,然后再对变换后的图像利用K_L和二维小波变换去除谱间冗余和空间冗余。小波编码采用两种编码方案:3D_SPIHT以及对它进行改进后的三维位平面的SPIHT算法,并对两者进行了比较。实验表明,采用直方图变换的这两种方法都获得了良好的效果,比没变换前有更好的图像质量和压缩性能。     

基于提升方案的多光谱遥感图像有损压缩算法

在分析多光谱遥感图像谱间和空间数据特点的基础上,提出了一种DPCM线性预测与基于提升方案的整数小波变换相结合的多光谱遥感图像有损压缩算法。在谱间采用DPCM预测去除谱间相关性;在谱内采用整数小波变换去除空间相关性,根据不同子带对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,并分别对各个子带量化后的数据和重要图表采用固定比特平面编码和游程编码,实现高效的多光谱遥感图像压缩。实验结果表明,该算法在一定的压缩比下,重构图像具有较高的峰值信噪比,并且算法硬件实现简单,对内存的需求低。     

Color Enhancement in Multispectral Image Using the Karhunen-Loeve Transform

We propose a new method for the color enhancement of multispectral image in the visible wavelength region. The purpose of the proposed method is to explore the weak features contained in a specific wavelength by discounting the major color distribution. Such examination will be valuable in visual inspection applications, for example, a medical examination using color image to find a small spectral change of an abnormal part. In this method, Karhunen-Loeve (KL) transform is applied to multispectral data, and specific wavelength components of only high-order KL coefficients are amplified while low-order coefficients are not changed to retain the major color distribution. In the experiment, this method was applied to multispectral images: a printed test image and a human skin image of a bruised arm were captured by a 16-band multispectral camera. The resultant images were compared with the images obtained by saturation enhancement and that obtained by applying the proposed method to the 3-band image. The method successfully visualized the features, which are almost invisible in natural color images, with less change in background color than saturation enhancement.     

三维多光谱数据压缩的新方法

提出了一种三维多光谱图像数据压缩的新方法，在对多光变图像数据进行了Ｋａｒｈｕｎｅｎ－Ｌｅｏｖｅ变换Ｋ－Ｌ变换后，再对变换后的多光谱图像数据进行静止图像方法压缩编码，消除谱和空间相关性。     

基于成像系统物理特性的多光谱图像与全色波段图像融合

提出了一种基于成像系统物理特性的多光谱图像与全色波段图像融合算法。该算法采用àtrous小波变换提取全色波段图像的空间细节信息,并将提取的空间信息按照一定的注入模型调整后添加到各波段多光谱图像中去,得到具有高空间分辨力的多光谱图像。注入模型充分考虑了各波段成像传感器的相对光谱响应函数、地表物体对各波段的光谱反射率以及各波段的辐射调整系数等成像系统的物理特性,使融合后的多光谱图像在显著提高空间质量的同时,最大可能地保留了原始多光谱图像的光谱特性。对IKONOS卫星遥感影像的融合实验结果表明,该算法在光谱保留和空间质量提高方面较其它基于小波变换的融合算法都具有更高的性能。     

Multispectral Image Compression Method by Karhunen-Loeve/JPEG Transformation

1.IntroductionThewideincreasingapplicationsofremotesensingmultispectralimagesrequirealotofmethodsforviewing,distributing,transmittingandstoringsuchimagedata.Multi-sPectralimagesneedtoanalyzeovermanysPectralbandsandchancetimedelayiscaused-ThedataflowscollectedbyLandsatThematicMapPer(TM)presentafurtherchallengeinimagedataprocessingwhileterabytes/dayofimageinformationisprocessed.InordertousethefinitebandwidthofsateIlitedown1ink(225Mbps)totransmitthesehugeimagedata,datacompressiononboardmustbe…     

色彩再现的多光谱图像压缩

针对多光谱图像在色度高保真再现等领域的应用,为提高压缩效率,进一步存储传输,提出了WF系列编码方法,设计了APWS_RA算法,进而提出了一种低复杂度、光照稳定性好且支持跨设备再现的WF_APWS_RA压缩算法。首先研究了现有的基于光谱均方误差的小波嵌入编码原理,提出了色感应失真准则和视觉特性矩阵W;同时,优化了APWS编码算法的码率分配,形成APWS_RA编码算法;最后,结合以上技术,以色度误差准则指导编码,对视觉加权后的多光谱图像数据进行aPWS_rA编码,命名为WF_APWS_RA。WF_APWS_RA算法融合人眼视觉特性矩阵W,利用吸引力传播(Affinity Propagation)聚类挖掘加权图像的谱间冗余,小波变换去除其空间冗余,最后结合误差补偿机制和码率分配策略进行小波编码。实验表明,在相同比特率下,较现有低复杂度经典编码算法,WF系列编码方法能更有效地保留光谱中的色度信息,APWS_RA算法的重建光谱误差最小,WF_APWS_RA算法的重建色度精度优势明显。     

基于非采样Contourlet变换的遥感图像融合算法

为了使融合后的多光谱图像在尽可能保持原始多光谱图像光谱特性的同时,显著提高空间分辨力,提出了一种基于非采样Contourlet变换(NSCT)的遥感图像融合算法。算法首先对全色波段图像进行非采样Contourlet变换,得到全色波段图像的低频子带系数和各带通方向子带系数;然后针对多光谱图像的每一个波段,将其进行双线性插值后作为融合后多光谱图像的低频子带系数,对全色波段图像的各带通方向子带系数采用基于成像系统物理特性的注入模型(调整系数)进行局部调整后,作为融合后多光谱图像的各带通方向子带系数,从而得到融合后多光谱图像的非采样Contourlet变换系数;最后再经非采样Contourlet逆变换得到该波段具有高空间分辨力的多光谱图像。采用IKONOS卫星遥感图像进行了仿真实验,实验结果表明,该算法在光谱保留和空间质量提高方面优于其它传统的遥感图像融合算法。     

基于1+2维小波变换的多光谱图像压缩

提出利用1+2维小波变换技术对多光谱遥感图像进行近无损压缩.使用嵌入式块截断算法压缩谱内图像;谱间变换使用短滤波器进行1维小波变换,去除多光谱图像的谱间冗余.实验证明,加入谱间的1维变换比不加入变换,而仅仅对每个波段图像进行压缩的压缩比提高20%.整个算法是嵌入式的,复杂度适中.编码的比特流具有渐进式特性,可以过渡为高压缩比的有损压缩.     

兼顾色度和光谱精度的多光谱图像LabW2P编解码

针对可见光多光谱图像在通用领域的应用,为提高压缩效率,有效提升重建光谱曲线的色度及光谱精度,进一步存储传输,提出了一种非线性光谱反射率模型,并基于此设计了复杂度适中、光照稳定性好且支持光谱跨设备再现的LabW2P编解码算法。首先根据多光谱图像物理特性,提出非线性光谱反射率模型,将光谱数据表示为线性成分和差别光谱,线性成分由六维变换空间及光谱投影系数组成,差别成分为非线性表示成分,该模型用于光谱数据至不同基变换空间的分解及表示,为算法的构建,光谱及色度重建性能的提升,提供了理论基础;然后,根据人眼视觉系统特征、光照条件,借助CIE标准色度空间转换函数,提取光谱反射率中的三维色度信息Lab,保证重建图像的色度精确性;基于光谱非线性表示模型,采用类视觉曲线的三角函数基,提取线性成分前两维投影系数作为光谱编码的后两维W₁和W₂,用于近似描述CIERGB色度空间中R和G通道,同时有效提高光谱数据的色度和光谱还原度;利用误差补偿机制生成预测差别光谱,采用主成分分析（PCA）法提取其第一维主成分作为编码值P,补偿了线性光谱重建误差,并进一步提升了光谱精确性;最后,组合提取的三部分数据,形成LabW2P编码。LabW2P解码即编码的逆过程。首先,根据Lab及W₁和W₂,结合CIELAB至CIEXYZ色度空间转换函数、光照条件、CIE标准观察者色匹配函数、及三角函数基,采用最小二乘回归,获得变换空间上的重建投影系数,进而重建线性光谱数据;然后,根据P值,采用PCA逆变换,获取重建预测差别数据,最后,结合两部分重建数据,获得光谱重建图像。实验分析显示,LabW2P算法的平均色度精度为0.207 6,较经典的PCA,LabPQR和LabRGB法分别提升了81.54%,55.48%,32.29%,最大平均色差为0.507 0,此外均处于0～0.5之间,达到了视觉难以辨认的可忽略色差的色彩重建水平;平均光谱精度为0.012 7,较PCA性能稍弱,较LabPQR和LabRGB法分别提升了13.01%,6.62%,表明LabW2P编码法的色度和光谱重建性能优势明显。此外该算法可直接用于物体色估计,较PCA和LabPQR法,传输附加信息少,可达压缩比更高。     

SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are introduced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional (1D) linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional (2D) integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ (WSFCVQ),the method of improved BiBlock zero tree coding (IBBZTC) and the method of feature spectral vector quantization (FSVQ), the peak signal-to-noise ratio (PSNR) of this method can enhance over 9 dB, and the total compression performance is improved greatly.     

基于宽色域色彩光谱样本集的色度校正方法研究

在跨媒体色彩再现流程中,受色彩再现场景光源及设备呈色机理等因素的影响,同一色彩信息在不同数字媒介之间进行传递时,往往存在色彩传递失真现象,该问题严重影响了现阶段影像色彩复制与传递的客观准确性。色度校正旨在克服上述色彩失真问题,进而提升影像色彩信息传递品质。现有色度校正方法主要可分为色适应变换类算法以及回归校正算法两大类,目前由于此两类方法所采用的训练样本普遍色域过小,故其往往导致在高饱和度色彩区域色度校正误差较大。为此,研究提出了一种基于宽色域色彩光谱样本集的新型色度校正方法。通过各类典型色彩光谱样本的收集与制备,构建了具有广阔色域范围的典型色彩光谱样本集,并在此基础上结合色域划分及色纯度寻优方法实现了现有回归校正模型建模样本的扩充与优化,显著提升了现阶段色度校正算法的校正精度。实验结果显示,在各类照明组合条件下,相比于现有色适应变换类方法以及回归校正类方法,所提出的色度校正方法可将以CIEDE2000色差公式表示的色度校正误差降低15%左右,对于高饱和度色彩区域其对应降幅可达40%左右。该方法的提出,将为未来数字影像色彩复制领域色彩再现技术的发展,提供有效的理论与方法支撑。     

基于可逆整数变换的高光谱图像无损压缩

将变换矩阵分解为三角可逆矩阵(TERM)实现的整数Karhunen-Loève变换(IKLT),具有结构简单、完全可逆和同址运算的优点.将整数KLT和整数小波结合(IWT),提出了一种基于可逆整数变换的去相关方法：将KLT用于去除谱间冗余,并在对KLT的变换矩阵进行TERM分解的过程中,提出基于全局最大值选择主元的优化分解方法,保证了IKLT的准确度,同时明显降低了计算量;空间维的去相关变换采用基于提升结构的整数小波变换,同样保证了变换的完全可逆.采用不同编码策略,对不同场景的高光谱图像数据压缩的实验结果表明,基于整数混合变换的去相关方法能明显提高无损压缩比.     

A Biblock Wavelet Zero Tree Coding for Hyperspectral Imagery Data Compression

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Spectral data compression using weighted principal component analysis with consideration of human visual system and light sources

This paper proposed two weight functions based on principal component analysis (PCA) to reserve more colorimetric information in spectral data compression process. One weight function consisted of the CIE XYZ color-matching functions representing the characteristic of the human visual system, while another was made up of the CIE XYZ color-matching functions of human visual system and relative spectral power distribution of the CIE standard illuminant D65. The improvement obtained from the proposed two methods were tested to compress and reconstruct the reflectance spectra of 1600 glossy Munsell color chips and 1950 Natural Color System color chips as well as six multispectral images. The performance was evaluated by the mean values of color difference under the CIE 1931 standard colorimetric observer and the CIE standard illuminant D65 and A. The mean values of root mean square errors between the original and reconstructed spectra were also calculated. The experimental results show that the proposed two methods significantly outperform the standard PCA and another two weighted PCA in the aspects of colorimetric reconstruction accuracy with very slight degradation in spectral reconstruction accuracy. In addition, weight functions with the CIE standard illuminant D65 can improve the colorimetric reconstruction accuracy compared to weight functions without the CIE standard illuminant D65.     

分组Karhun-Loeve变换/整数小波变换高光谱影像准无损压缩新方法

提出了分组Karhunen-Leove变换(KLT)和整数小波变换(IWT)的高光谱图像数据压缩方法,并采用整数小波变换技术和Set Partitioning in Hierarchical Trees(SPIHT)压缩编码,实现了对分组Karhun-Loeve变换后的数据压缩。该压缩编码方法与现有压缩方法相比,既保留了Karhun-Loeve变换压缩性能和整数小波变换高压缩比的特点,也宜于实时传输。实验结果表明,分组Karhun-Loeve变换/整数小波变换/SPIHT在相同压缩比下,峰值信噪比比Karhun-Loeve变换/小波变换/WSFCVQ、Karhun-Loeve变换/小波变换/改进的对块零树编码压缩和Karhun-Loeve变换/WT/FSVQ分别提高了6 dB,9 dB和8 dB,运算时间减少一半,整体压缩性能有了较大的提高。     

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.     

A Biblock Wavelet Zero Tree Coding for Hyperspectral Imagery Data Compression

In this paper, a biblock zero tree compression coding (BBZTC) method, based on wavelet zero tree compression coding (ZTC), is used to exploit redundancy in hyperspectral imagery data. Because of ZTC scanning every wavelet zero tree coefficients with low efficiency, BBZTC method outperforms ZTC in the aspects of high compression ratio, simple real-time implementing, and higher coding/decoding speed, and convenient real-time transmission. The experimental results show that this method can obtain the compression ratio 17～40 times of 224 spectral bands to remove the spectral correlation without any coding by using KLT, and the total compression performance of KLT+BBZTC method is better than the method of KLT and other one dimensional transformation (such as DCT) to remove the spectral correlation. To compare with the total compression ratio of KLT+JPEG method and SFCVQ method, this method reaches 180 at the PSNR of 33.6 db. This method is superior to the present any other method at compression rate.