基于分类和3D-SPIHT算法相结合的多光谱图像压缩

文中提出了一种基于分类预测的三维SPIHT算法,并对多光谱1~7波段图像进行了压缩实验。首先对图像数据作三维变换,空域采用浮点97小波去除相关性,谱域分类预测去除冗余;再根据分类预测算法获得系数的残差图像,并对残差图像进行三维SPIHT编码; 而对分类预测时得到的码书和索引表进行哈夫曼无损压缩; 将这3个编码文件传送到解码端用于图像重构。实验证明该算法具有很好的重构效果。     

Medical image compression by using three-dimensional wavelet transformation

This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB, In general, the smaller the slice distance, the better the 3-D compression performance.     

基于改进的K-L变换的多光谱图像压缩算法

融合离散小波变换(DWT,discrete wavelet tran sform)与Karhunen-Loeve变换(KLT),将图像的能量集中到少数系数上,以达到更好的 压缩效果。首先将多光谱图像的每个谱段进行快速9/72D DWT,消除多光谱图像的大部分 空间冗余;然后对所有谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空 间冗余;最后对所得谱段产生的小波系数进行改进的KLT,来消除光谱冗余和残存的空间冗 余;最后对所得系数进行熵编码,得到压缩码流。实验结果表明,在码率为0.25～2.0bit/ pixel范围内,平均信噪比(SNR)高于41dB,同时缩短了运 算时间,从而提升了多光谱图像压 缩算法的性能。     

Adaptive fuzzy segmentation of magnetic resonance images

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the 2-D adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new faster multigrid-based algorithm for its implementation. We show, using simulated MR data, that 3-D AFCM yields lower error rates than both the standard fuzzy C-means (FCM) algorithm and two other competing methods, when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.     

Optimal transforms for multispectral and multilayer image coding

Multispectral images are composed of a series of images at differing optical wavelengths. Since these images can be quite large, they invite efficient source coding schemes for reducing storage and transmission requirements. Because multispectral images include a third (spectral) dimension with nonstationary behavior, these multilayer data sets require specialized coding techniques. The authors develop both a theory and specific methods for performing optimal transform coding of multispectral images. The theory is based on the assumption that a multispectral image may be modeled as a set of jointly stationary Gaussian random processes. Therefore, the methods may be applied to any multilayer data set which meets this assumption. Although the authors do not assume the autocorrelation has a separable form, they show that the optimal transform for coding has a partially separable structure. In particular, they prove that a coding scheme consisting of a frequency transform within each layer followed by a separate KL transform across the layers at each spatial frequency is asymptotically optimal as the block size becomes large. Two simplifications of this method are also shown to be asymptotically optimal if the data can be assumed to satisfy additional constraints. The proposed coding techniques are then implemented using subband filtering methods, and the various algorithms are tested on multispectral images to determine their relative performance characteristics.     

基于NSCT的多光谱和全色图像的融合

提出了一种基于无下采样Contourlet变换的多光谱和全色图像的融合方法.该方法在对多光谱影像进行IHS变换的基础上,对多光谱的I分量和高分辨率的全色影像分别进行无下采样Contourlet变换(NSCT),然后对分解得到的近似分量以及各层金字塔各方向的细节分量利用本文提出的一定的融合准则分别对近似分量和细节分量进行影像融合,最后通过无下采样Conlourlet逆变换得到新的I分量,与H,S分量一起还原到RGB空间,最终得到融合后的高分辨率多光谱彩色图像.本文采用一组多光谱图像和全色图像数据进行融合实验,其实验融合图像的目视效果和统计指标均优于传统的IHS融合方法、小波融合方法以及Contourlet变换方法.     

An orthogonal wavelet representation of multivalued images

A new orthogonal wavelet representation of multivalued images is presented. The idea for this representation is based on the concept of maximal gradient of multivalued images. This concept is generalized from gradients toward linear vector operators in the image plane with equal components along rows and columns. Using this generalization, the pyramidal dyadic wavelet transform algorithm using quadrature mirror filters is modified to be applied to multivalued images. This results in a representation of a single image, containing multiscale detail information from all component images involved. This representation leads to multiple applications ranging from multispectral image fusion to color and multivalued image enhancement, denoising and segmentation. In this paper, the representation is applied for fusion of images. More in particular, we introduce a scheme to merge high spatial resolution greylevel images with low spatial resolution multivalued images to improve spatial resolution of the latter while preserving spectral resolution. Two applications are studied: demosaicing of color images and merging of multispectral remote sensing images.     

Region-based image coding with multiple algorithms

The wide usage of small satellite imagery, especially its commercialization, makes data-based onboard compression not only meaningful but also necessary in order to solve the bottleneck between the huge volume of data generated onboard and the very limited downlink bandwidth. The authors propose a method that encodes different regions with different algorithms. The authors use three shape-adaptive image compression algorithms as the candidates. The first one is a JPEG-based algorithm, the second one is based on the object-based wavelet transform method proposed by Katata et al. (1997), and the third adopts Hilbert scanning of the regions of interest followed by one-dimensional (1-D) wavelet transform. The three algorithms are also applied to the full image so that one can compare their performance on a whole rectangular image. The authors use eight Landsat TM multispectral images and another 12 small satellite single-band images as their data set. The results show that these compression algorithms have significantly different performance for different regions     

结合矢量量化的SPIHT算法用于多光谱图像压缩

针对多波段遥感图像纹理复杂丰富、局部相关性较弱的特点,提出了结合矢量量化的SPIHT压缩算法。将经过小波变换后的遥感图像谱间相同位置的系数聚集构成矢量,根据高频子图的局部块纹理强弱进行自适应性的量化。使基于标量的SPIHT算法能够方便的处理矢量,有效去除数据间各类相关。实验表明,该方法对多波段遥感图像的压缩可以收到良好的效果,且算法具有良好的实时性,对单幅图像的压缩比和峰值信噪比(PSNR)均优于普通的二维SPIHT算法。     

一种多分辨率图像混合编码方案

本文提出一种基于小波变换与神经网络的多分辨率图像混合编码方案，利用小波分解对图像的多分辨率表示来消除图像空间域和频率域的相关性，由于小波图像相邻行之间的复杂关系难以用线性表示式来描述，使用多层神经网络（ＭＬＮＮ）来确定这种未知关系。实验证明，神经网络非线性预测器性能优于线性预测器，对非线性预测后的差值图像用自组织特征映射（ＳＯＦＭ）码书进行矢量量化（ＶＱ）编码，编码图像主观质量好，压缩比高，算法简     

Edge-preserving image compression using adaptive lifting wavelet transform

In this paper, a novel 2-D adaptive lifting wavelet transform is presented. The proposed algorithm is designed to further reduce the high-frequency energy of wavelet transform, improve the image compression efficiency and preserve the edge or texture of original images more effectively. In this paper, a new optional direction set, covering the surrounding integer pixels and sub-pixels, is designed. Hence, our algorithm adapts far better to the image orientation features in local image blocks. To obtain the computationally efficient and coding performance, the complete processes of 2-D adaptive lifting wavelet transform is introduced and implemented. Compared with the traditional lifting-based wavelet transform, the adaptive directional lifting and the direction-adaptive discrete wavelet transform, the new structure reduces the high-frequency wavelet coefficients more effectively, and the texture structures of the reconstructed images are more refined and clear than that of the other methods. The peak signal-to-noise ratio and the subjective quality of the reconstructed images are significantly improved.     

Image coding using wavelet transform and classified vectorquantisation

The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and classified vector quantisation is presented. The input image is first decomposed into a hierarchy of three layers containing ten subimages by the discrete wavelet transform. The lowest resolution low frequency subimage is scalar quantised with 8 bits/pixel. The high frequency subimages are compressed by classified vector quantisation to utilise the crosscorrelation among different resolutions while reducing the edge distortion and computational complexity. Vectors are constructed by combining the corresponding wavelet coefficients of different resolutions in the same orientation and classified according to the magnitude and the position of wavelet transform coefficients. Simulation results show that the proposed scheme has a better performance than those utilising current scalar or vector quantisation schemes     

Adaptive directional lifting-based wavelet transform for image coding.

We present a novel 2-D wavelet transform scheme of adaptive directional lifting (ADL) in image coding. Instead of alternately applying horizontal and vertical lifting, as in present practice, ADL performs lifting-based prediction in local windows in the direction of high pixel correlation. Hence, it adapts far better to the image orientation features in local windows. The ADL transform is achieved by existing 1-D wavelets and is seamlessly integrated into the global wavelet transform. The predicting and updating signals of ADL can be derived even at the fractional pixel precision level to achieve high directional resolution, while still maintaining perfect reconstruction. To enhance the ADL performance, a rate-distortion optimized directional segmentation scheme is also proposed to form and code a hierarchical image partition adapting to local features. Experimental results show that the proposed ADL-based image coding technique outperforms JPEG 2000 in both PSNR and visual quality, with the improvement up to 2.0 dB on images with rich orientation features.     

自适应小波变换及其在JPEG 2000中的应用

针对传统的线性小波分解不能很好保留图像边缘信息．且在跃变点处会出现大的小波系数而不利于图像压缩，提出了一种新的基于提升方案的二维不可分离自适应小波变换（AWT）方案。在该方案中，根据局部信息自适应地将图像分为平滑区域和非平滑区域．在不同区域选取不同的更新和预测函数。通过在JPEG2000中的实验结果表明，用该方法进行图像分解。低频近似图像保留了细节信息，边缘清晰；对双边AWT后的图像压缩，性能优于JPEG2000的5／3小波和9／7小波，峰值信噪比（PSNR）提高0．5～2．0dB。     

MULTI-SPECTRAL AND HYPERSPECTRAL IMAGE FUSION USING 3-D WAVELET TRANSFORM

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspeetral image. According to the characteristics and 3-Dimensional （3-D） feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures： Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling （RIBSR）method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution （A＆S） rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A＆S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.     

MODIS多光谱图像压缩研究

为有效存储MODIS多光谱图像数据,该文提出一种基于谱间预测和整数小波变换的多光谱图像压缩算法.首先通过构造谱间最优预测器去除谱间冗余,再利用整数小波变换和SPIHT算法对预测误差图像去除空间冗余,最后进行自适应算术编码.该方法可实现MODIS多光谱图像的无损、近无损和有损压缩,取得了满意的实验结果;在不同小波基条件下与3D-SPIHT算法比较,表明了该方法的有效性.     

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.     

Simultaneous vector-valued image segmentation and intensity nonuniformity correction using variational level set combined with Markov random field modeling

Object quantification requires an image segmentation to make measurements about size, material composition and morphology of the object. In vector-valued or multispectral images, each image channel has its signal characteristics and provides special information that may improve the results of image segmentation method. This paper presents a region-based active contour model for vector-valued image segmentation with a variational level set formulation. In this model, the local image intensities are characterized using Gaussian distributions with different means and variances. Furthermore, by utilizing Markov random field, the spatial correlation between neighboring pixels and voxels is modeled. With incorporation of intensity nonuniformity model, our method is able to deal with brain tissue segmentation from multispectral magnetic resonance (MR) images. Our experiments on synthetic images and multispectral cerebral MR images with different noise and bias level show the advantages of the proposed method.     

Slant Transform Image Coding

A new unitary transform called the slant transform, specifically designed for image coding, has been developed. The transformation possesses a discrete sawtoothlike basis vector which efficiently represents linear brightness variations along an image line. A fast computational algorithm has been found for the transformation. The slant transformation has been utilized in several transform image-coding systems for monochrome and color images. Computer simulation results indicate that good quality coding can be accomplished with about 1 to 2 bits/pixel for monochrome images and 2 to 3 bits/pixel for color images.     

State-dependent vector hybrid linear and nonlinear ARMA modeling: Applications

In a recent companion paper, a new method has been presented for modeling general vector nonstationary and nonlinear processes based on a state-dependent vector hybrid linear and nonlinear autoregressive moving average (SVH-ARMA) model. This paper discusses some potential applications of the SVH-ARMA model, including signal filtering, time series prediction, and system control. First, a state-space model governed by a hidden Markov Chain is shown to be equivalent to the SVH-ARMA model. Based on this state-space model, the extended Kalman filtering and Bayesian estimation techniques are applied for noisy signal enhancement. The result of a noisy image enhancement verifies that the model can track the time-varying statistical characteristics of nonstationary and nonlinear processes adaptively. Second, the SVH-ARMA model is used for a vector time series prediction, which can attain more accurate multiple step ahead prediction, than conventional forecasting methods. Third, a new technique is developed for predicting scalar long correlation time series in the wavelet scale space domain based on the SVH-ARMA model. Dyadic wavelet transform is employed to convert a scalar time series to a vector time series, to which the SVH-ARMA model is applied for vector time series prediction. More accurate and robust forecasting results in both one step and multiple step ahead prediction can be gained. See also the companion paper on theory, by Zheng et al., pp. 551–574, this issue.