Dynamic image data compression in the spatial and temporal domains: clinical issues and assessment

In our previous work, we developed a novel approach to dynamic image data compression, and demonstrated that very high compression ratios can be achieved while preserving relevant kinetic information. However, the technique has not yet been assessed with clinical data. Many issues need to be addressed to tailor the method for clinical use. In this paper, we apply the compression technique to dynamic [18F] 2-fluoro-deoxy-glucose (FDG) brain positron emission tomography (PET) data, using a five-parameter model to include cerebral blood volume (CBV) and partial volume (PV) effects. Functional images generated from the compressed data are compared with those from the original uncompressed data. We show that the storage requirements for a typical clinical dynamic PET image data set can be reduced by more than 95%, without degradation of image quality. Furthermore, the technique greatly reduces the computational complexity of further clinical image postprocessing such as smoothing and generation of functional images. It is expected that the compression technique will be of benefit in image data management and telemedicine.     

Fractal image compression based on spatial correlation and hybrid genetic algorithm

In order to solve the high complexity of the conventional encoding scheme for fractal image compression, a spatial correlation hybrid genetic algorithm based on the characteristics of fractal and partitioned iterated function system (PIFS) is proposed in this paper. There are two stages for the algorithm: (1) Make use of spatial correlation in images for both range and domain pool to exploit local optima. (2) Adopt simulated annealing genetic algorithm (SAGA) to explore the global optima if the local optima are not satisfied. In order to avoid premature convergence, the algorithm adopt dyadic mutation operator to take place of the traditional one. Experiment results show that the algorithm convergent rapidly. At the premise of good quality of the reconstructed image, the algorithm saved the encoding time and obtained high compression ratio.     

Listless zerotree image compression algorithm

Recent years ,wavelet transformhas been widely usedin i mage compression because of its outstanding ti me-frequency localization ability and de-correlation ability.Among all the i mage compression algorithms ,EZW[1]and SPIHT[2]are two of the most influent…     

Block-based SVD image watermarking in spatial and transform domains

The idea of this paper is to implement an efficient block-by-block singular value (SV) decomposition digital image watermarking algorithm, which is implemented in both the spatial and transforms domains. The discrete wavelet transform (DWT), the discrete cosine transform and the discrete Fourier transform are exploited for this purpose. The original image or one of its transforms is segmented into non-overlapping blocks, and consequently the image to be inserted as a watermark is embedded in the SVs of these blocks. Embedding the watermark on a block-by-block manner ensures security and robustness to attacks such like Gaussian noise, cropping and compression. The proposed algorithm can also be used for colour image watermarking. A comparison study between the proposed block-based watermarking algorithm and the method of Liu is performed for watermarking in all domains. Simulation results ensure that the proposed algorithm is more effective than the traditional method of Liu, especially when the watermarking is performed in the DWT domain.     

Decoding algorithm for fractal image compression

A new iterative decoding method is proposed for fractal image compression. Convergence properties are provided. Experimental results show the superiority of the new method over the conventional decoding procedure     

一种新的图像压缩编码算法

对图像压缩编码算法进行了改进。首先,将小波分解后的3个高频系数进行预处理:将高频部分进行球坐标变换,降低了同一尺度内系数的相关性;基于小波域和球坐标域的两个前提,定义了多尺度模积的概念,用来控制收缩函数对小波高频部分进行收缩处理。这样,可以去除那些不影响视觉效果的小波系数以及噪声信息,达到较高的压缩比。然后,对小波变换的低频部分进行单独编码(DPCM),对球坐标下的高频部分采用改进的多级树集合分裂(SPIHT)编码。针对SPIHT编码中重复扫描的问题,引入了最大值矩阵MMP(matrix of maximum pixel),这种策略能够有效降低比较次数。仿真实验表明,本文提出的算法具有较好的编码效率。     

一种改进的分形图像压缩算法

为了缩短分形编码时间,通过对图像定义域块和值域块的统计特性分析,提出了改进的分形压缩算法。设计最优匹配定义域块自适应搜索方法,缩短搜索范围;用值域块均值代替灰度偏移量,减少计算量。实验证明,运用这种改进的分形图像压缩算法进行图像压缩,在保持较高的解压图像质量的情况下,大幅缩短图像压缩编码时间。     

An image compression method for spatial search

The maintenance of large raster images under spatial operations is still a major performance bottleneck. For reasons of storage space, images in a collection, such as satellite pictures in geographic information systems, are maintained in compressed form. Instead of performing a spatially selective operation on an image by first decompressing the compressed version, we propose to perform queries directly on the compressed version of the image. We suggest a compression technique that allows for the subsequent use of a spatial index structure to guide a spatial search. In response to a window query, our algorithm delivers a compressed partial image, or the exact uncompressed requested image region. In addition to the support of spatial queries on compressed continuous tone images, the new compression algorithm is even competitive in terms of the compression ratio that it achieves, compared to other standard lossless compression techniques.     

Modified VQ-BTC algorithm for image compression

VQ-BTC is a recent technique used in the coding of image data to combat edge degradation produced by vector quantisation (VQ) or block truncation coding (BTC). However, it has high encoding complexity and needs a large amount of memory to store 31 codebooks at both the encoder and decoder. A modified VQ-BTC (MVQ-BTC) algorithm is presented which achieves a performance close to that of VQ-BTC, but needs only three codebooks, and requires less computation time than VQ-BTC     

Video compression employing algorithm using temporal blockingstructures

A new video compression algorithm based on a temporal blocking structure, rather than the more conventional spatial blocking structure, is described. This blocking structure forms the basis of an adaptive vector quantisation (VQ) algorithm, the performance of which is then compared with a similar adaptive VQ scheme based on a spatial blocking structure     

Lossless compression of multispectral image data

While spatial correlations are adequately exploited by standard lossless image compression techniques, little success has been attained in exploiting spectral correlations when dealing with multispectral image data. The authors present some new lossless image compression techniques that capture spectral correlations as well as spatial correlation in a simple and elegant manner. The schemes are based on the notion of a prediction tree, which defines a noncausal prediction model for an image. The authors present a backward adaptive technique and a forward adaptive technique. They then give a computationally efficient way of approximating the backward adaptive technique. The approximation gives good results and is extremely easy to compute. Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations. Furthermore, the increase in complexity incurred in order to make these gains is minimal     

高速采集图像实时压缩算法

为了解决高速图像采集给图像数据的实时传输和存储带来的困难,提出了一种基于提升形态Haar小波的面向自动目标识别(ATR)的图像压缩算法——目标形态小波图像编码(TMWC).该算法将形态Haar小波与数学形态学方法结合应用.首先将采集图像进行二维提升形态Haar小波分解,结合目标感兴趣区域(ROI)检测要求的特点,仅在尺...     

Normalization schemes in a neural network image compression algorithm

Recently multilayer neural networks have been used for still picture compression. In these networks it is necessary to normalize the gray levels in the input picture before they are fed into the neural network. In this paper we investigate six different normalization functions, of which four are new and appear for the first time in this paper. We show that the compression efficiency of a neural network depends on the normalization function used and that the new normalization functions consistently outperform the traditional normalization functions.     

超光谱图像数据压缩方法综述

针对目前超光谱图像的编码压缩还没有一个公认的标准或已成熟的压缩方法，本文对主要的几类超光谱图像压缩方法进行了介绍，并给出了各类压缩方法无损压缩的压缩比。随着对超光谱图像压缩研究的深入，多种方法结合使用的趋势愈加明显。通过对各种超光谱压缩技术的分析比较，为超光谱压缩算法的研究奠定了良好基础。     

一种基于小波系数重要图编码的图像压缩算法

提出了一种新的基于小波系数重要图编码的图像压缩算法。该算法根据量化后的波系数的特点进行了一种期望排序,然后舍掉序列后面大量的零值小波系数,从而得到一个波系数子集,能以少的小波系数来很好地逼近原始图像,省去了零树编码中零树结构带来的大量比特开销。实验表明,该算法与MPEG-4的静止图像压缩算法相比较,重构图像的峰值信噪比（PSNR）值在相同码率下有较大的提高。     

一种基于LBT的分布式图像压缩算法

无线多媒体传感器网络(WMSNs)中传感器节点采集的数据量非常大,在传输前需对大数据量的多媒体信息进行压缩处理,但是单节点能源受限,存储、处理能力相对较弱。针对无线多媒体传感器网络应用的高效、低耗能的需求这些问题,在图像压缩双正交重叠变换(LBT)的基础上,文中提出了一种基于此变换的分布式无线多媒体传感器网络图像压缩算法。即基于簇结构,把压缩任务分配给其他节点,通过多个节点相互协作,共同完成图像的压缩编码和传输。实验结果表明,在传感器节点散布不均且较为密集的情况下,该算法在高质量、低复杂度和低功耗等方面都有了很大的性能提高。     

基于灰度值分割的感兴趣区域图像压缩算法

基于JPEG2000中感兴趣区域(ROI)图像压缩标准,提出了一种新型ROI图像压缩算法。算法根据用户指定的ROI条件,确定灰度值的重要位平面,然后将灰度值分割为重要灰度值和次要灰度值,优先对重要灰度值进行编码。根据比特率要求对次要灰度值进一步编码。实验结果表明:提出的算法同时具有最大平移法和一般平移法的优点,不仅能够灵活控制ROI和背景区域图像质量在码率上的分配,还能支持任意形状的ROI编码而不需要编码形状信息。另外,由于在小波变换前就进行了灰度值分割,所以在低码率时能够有效提高计算速度并节省存储空间。     

Technique for fractal image compression using genetic algorithm

A new method for fractal image compression is proposed using genetic algorithm (GA) with an elitist model. The self transformation property of images is assumed and exploited in the fractal image compression technique. The technique described utilizes the GA, which greatly decreases the search space for finding the self similarities in the given image. This article presents theory, implementation, and an analytical study of the proposed method along with a simple classification scheme. A comparison with other fractal-based image compression methods is also reported.