基于小波变换的JPEG2000压缩实现

JPEG2000算法可以同时兼容无损压缩和有损压缩。本文描述了JEPG2000算法及一种基于小波的图像压缩的具体实现，并将压缩后的图像在图像的质量及大小两方面与原始图像作了简单的比较，便于选用不同的压缩比得到适合的图像。     

基于时域变换的失真度可调图像压缩算法

提出一种基于时域变换的失真度可调图像压缩算法，在象素合并过程中，利用一个最大均方差阈值和一个粒度差值控制象素节点和结构节点的产生，来生成四叉树结构及象素数据表，通过调整阈值和差值的具体取值（0－40）来实现不同的压缩比（从无损压缩连续变化到大比例有损压缩），压缩后的图像在相同PSNR情况下比JPEG压缩占有更少的空间，在相同压缩比的情况下图像质量高于用JPEG压缩的图像，该算法还有良好的时间复杂性O（N／3）和空间复杂性O（N），可用于任意大小图像的压缩与传输。     

基于DCT快速变换的图像压缩编码算法

在JPEG标准中,基于图像压缩的有损压缩算法中的离散余弦变换（DCT）,应用于很多图像压缩场合,并且在实际操作中,能获得较高的压缩比,同时压缩后的图像与原始图像的视觉效果基本相同,因此得到了广泛应用。为了达到提高图像质量的目的,文中提出了一个基于二维离散余弦变换（DCT）的图像压缩改进算法,该算法通过设置量化系数来控制图像压缩数组的大小。同时,在图像压缩部分利用DCT快速算法。仿真实验结果表明：该算法进一步提高了图像的峰值信噪比（PSNR）和主观视觉质量。     

基于JPEG的固定背景视频压缩算法研究

为了对固定背景视频进行压缩并获得较高的压缩比,在JPEG静止图像压缩标准的基础上提出了一种新的应用于固定背景视频压缩的算法.对第一帧图像进行JPEG格式的压缩并保存量化后的离散余弦变换系数,对第一帧后的每一帧图像,在进行离散余弦变换和量化后,先同存储器内的第一帧图像的离散余弦变换系数进行异或运算再进行熵编码.通过使用该算法和H.264视频压缩标准对同一段固定背景视频进行压缩并比较压缩后的数据量,表明了该算法具有较高的压缩比.     

JPEG2000在低比特率图像压缩的研究

介绍了JPEG2000的主要特点和基本架构,以及JPEG2000编码器的两个重要部分:小波变换和熵编码.简明分析了小波变换DWT与离散余弦变换DCT的区别和小渡变换的优点.详细介绍了JPEG2000的核心编码算法EBCOT.着重介绍了JPEG2000在图像低比特率情况下的应用,并进行了实际应用的实验:在图像压缩比很高时,JPEG2000与JPEG的压缩效果对比图.     

基于JPEG 2000的医学图像ROI压缩

本文简单介绍了无损压缩和有损压缩及其特点,重点介绍了基于JPEG2000的感兴趣区域(regionofinterestROI)的压缩原理及实现方案,并用实验进行验证,将压缩后的图像与原始图像作了比较,便于医务人员选用不同的压缩比得到适合的图像以进行更准确的诊断。     

用于TF/TA的机载数字地形数据压缩算法研究

针对海量地形数据在低空突防飞机上的存储问题,提出了一种适用于TF/TA的机载数字地形高程数据压缩的算法;该算法首先对地形高程数据进行简单曲面拟合,对拟合前后的地形高程数据进行对比,计算残差,判定残差范围是否在飞机飞行的安全范围,对必须保留的残差数据进行基于DCT变换的JPEG有损压缩;最后,针对平坦与复杂地形数据提出了不同的压缩算法,验证了曲面拟合和JPEG有损压缩两种算法结合的可行性,并给出了实验评价,试验结果得到了比单纯JPEG压缩更好的压缩比。     

主观评价JPEG与JPEG2000标准的彩色图像最佳压缩比的实验研究

研究压缩图像在显示器上质量主观评价方法,为实现Internet上图像质量主观评价奠定基础。研究包括显示器设置问题;在彩色电视图像质量主观评价ITU-BT500-11标准的基础上,提出静态压缩图像评价指标;收集了标准彩色测试图像并建立图像库。最后使用JPEG与JPEG2000两种压缩标准对具有不同性质的20景彩色图像进行了7种压缩比的有损压缩,并选取10人采用成对比较的视觉心理物理学方法,对压缩图像进行主观评价实验。得出了JPEG标准下彩色图像最佳压缩比范围为20：1~25：1,JPEG2000范围为25：1~30：1,同时实验结果表明,可以通过视觉的方法对不同LCD显示器进行设置,并达到一个大致相同的设置效果。     

基于C语言的图像压缩算法

该文借鉴静态图像压缩标准JPEG的理论研究成果,将其与DCT快速变换相结合,采用霍夫曼编码方法,用C语言编程实现灰度图像的压缩。最后,计算了基于DCT快速变换的图像压缩算法的压缩比。同时,分析了DCT快速变换后的数据,验证了该算法用于图像压缩的合理性。     

基于C语言的图像压缩算法

该文借鉴静态图像压缩标准JPEG的理论研究成果,将其与DCT快速变换相结合,采用霍夫曼编码方法,用C语言编程实现灰度图像的压缩。最后,计算了基于DCT快速变换的图像压缩算法的压缩比。同时,分析了DCT快速变换后的数据,验证了该算法用于图像压缩的合理性。     

Lossy image compression using singular value decomposition and wavelet difference reduction

This paper presents a new lossy image compression technique which uses singular value decomposition (SVD) and wavelet difference reduction (WDR). These two techniques are combined in order for the SVD compression to boost the performance of the WDR compression. SVD compression offers very high image quality but low compression ratios; on the other hand, WDR compression offers high compression. In the Proposed technique, an input image is first compressed using SVD and then compressed again using WDR. The WDR technique is further used to obtain the required compression ratio of the overall system. The proposed image compression technique was tested on several test images and the result compared with those of WDR and JPEG2000. The quantitative and visual results are showing the superiority of the proposed compression technique over the aforementioned compression techniques. The PSNR at compression ratio of 80:1 for Goldhill is 33.37 dB for the proposed technique which is 5.68 dB and 5.65 dB higher than JPEG2000 and WDR techniques respectively.     

Impact of lossy compression on mapping crop areas from remote sensing

This study measures the effect of lossy image compression (JPEG 2000 and JPG) on the digital classification of crop areas. The results provide new insights into the influence of compression on the quality of the cartography produced. Both a multitemporal and a single-date classification approach were analysed. With the multitemporal approach, it is possible to use high compression ratios (CRs), up to 20:1 or even 100:1, and the overall accuracy of the classification is similar to that obtained with the original images. Moreover, the classified area is similar or even greater (fewer pixels are uncertain). For a single-date approach, it is only advisable to use 3D-JPEG 2000 at CRs up to 20:1. The optimum CR is also affected by landscape fragmentation (fragmented images tolerate less compression) and the classification method (hybrid classifiers are affected less than the maximum likelihood and minimum distance classifiers). Finally, classifications from compressed images have less ‘salt and pepper’ effect than those obtained from the originals, especially when JPEG 2000 (3D or not) is used.     

An HVS based adaptive quantization scheme for the compression of color images

In this paper a Human Visual System based adaptive quantization scheme is proposed. The proposed algorithm supports perceptually lossless as well as lossy compression. The algorithm uses a transform based compression approach using the wavelet transform, and has incorporated vision models for the compression of both luminance and chrominance components. The major strength of the coder is the incorporation of the vision model for the chrominance components and the optimum way in which the scales are distributed among the luminance and chrominance components to achieve higher compression ratios. The perceptual model developed for the color components gives flexibility for giving more compression for the color components without causing any color degradations. For each image the visual thresholds are evaluated and an optimum bit allocation is done in such a way that the quantization error is always less than the visual distortion for the given rate. To validate the strength of the proposed algorithm, the perceptual quality of the images reconstructed using the proposed coder is compared with the images reconstructed with JPEG2000 standard coder, for the same compression. To evaluate the perceptual quality of the compressed images latest perceptual quality matrices such as Structural Similarity Index, Visual Information Fidelity and Visual Signal-to-Noise Ratio are used. The results obtained reveal that the proposed structure gives excellent improvement in perceptual quality compared to the existing schemes, for both lossy as well as lossless compression. These advantages make the proposed algorithm a good candidate for replacing the quantizer stage of the current image compression standards.     

基于JPEG2000压缩域的图像信息隐藏方法

为了与图像压缩标准JPEG2000相适应，提出了两种可直接在JPEG2000压缩域内进行信息隐藏的方法，分别采用JPEG2000编码过程和解码过程进行实现。它们既支持无损压缩，也适用于有损压缩，从而具有较广的适用范围。实验结果表明这两种实现方法具有较好的不可见性和压缩性。     

Cloud enabled fractal based ECG compression in wireless body sensor networks

E-health applications deal with a huge amount of biological signals such as ECG generated by body sensor networks (BSN). Moreover, many healthcare organizations require access to these records. Therefore, cloud is widely used in healthcare systems to serve as a central service repository. To minimize the traffic going to and coming from cloud ECG compression is one of the proposed solutions to overcome this problem. In this paper, a new fractal based ECG lossy compression technique is proposed. It is found that the ECG signal self-similarity characteristic can be used efficiently to achieve high compression ratios. The proposed technique is based on modifying the popular fractal model to be used in compression in conjunction with the iterated function system. The ECG signal is divided into equal blocks called range blocks. Subsequently, another down-sampled copy of the ECG signal is created which is called domain. For each range block the most similar block in the domain is found. As a result, fractal coefficients (i.e. parameters defining fractal compression model) are calculated and stored inside the compressed file for each ECG signal range block. In order to make our technique cloud friendly, the decompression operation is designed in such a way that allows the user to retrieve part of the file (i.e. ECG segment) without decompressing the whole file. Therefore, the clients do not need to download the full compressed file before they can view the result. The proposed algorithm has been implemented and compared with other existing lossy ECG compression techniques. It is found that the proposed technique can achieve a higher compression ratio of 40 with lower Percentage Residual Difference (PRD) Value less than 1%.     

Model-Based Power Aware Compression Algorithms for MPEG-4 Virtual Human Animation in Mobile Environments

MPEG-4 body animation parameters (BAP) are used for animation of MPEG-4 compliant virtual human-like characters. Distributed virtual reality applications and networked games on mobile computers require access to locally stored or streamed compressed BAP data. Existing MPEG-4 BAP compression techniques are inefficient for streaming, or storing, BAP data on mobile computers, because: 1) MPEG-4 compressed BAP data entails a significant number of CPU cycles, hence significant, unacceptable power consumption, for the purpose of decompression, 2) the lossy MPEG-4 technique of frame dropping to reduce network throughput during streaming leads to unacceptable animation degradation, and 3) lossy MPEG-4 compression does not exploit structural information in the virtual human model. In this article, we propose two novel algorithms for lossy compression of BAP data, termed as BAP-Indexing and BAP-Sparsing. We demonstrate how an efficient combination of the two algorithms results in a lower network bandwidth requirement and reduced power for data decompression at the client end when compared to MPEG-4 compression. The algorithm exploits the structural information in the virtual human model, thus maintaining visually acceptable quality of the resulting animation upon decompression. Consequently, the hybrid algorithm for BAP data compression is ideal for streaming of motion animation data to power- and network-constrained mobile computers     

面向任务的医学图象压缩

现代医学成象技术产生了大量的医学数字图象,而这些图象的存储和传输却存在很大问题,传统上,采用无损压缩编码方法改善这些图象的存储和传输效率,全为了达到较高的压缩比,必须采用有损压缩,然而,有损压缩会给图象带来失真,必须谨慎使用,医学图象通常,由二类区域构成,其中一类包含重要的诊断信息,由于其错误描述的代价非常高,因此提供一种高重的质量的压缩方法更加必要,另一类区域的信息较为次要,其压缩的目标则要求达到尽可能高的压缩比,为了既能保证感兴趣区图象的重构质量,又能获得较高压缩比,提出了一种面向任务的医学图象压缩算法,该方法把无损压缩和有损压缩统一在小波变换的框架下,对感兴趣区采用无损压缩,而对其他部分则采用有损压缩,实验证明,该压缩方法在压缩比和重建图象质量上均达到了较好的性能。     

High-resolution image compression algorithms in remote sensing imaging

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000.     

Evaluating image quality measures to assess the impact of lossy data compression applied to climate simulation data

Applying lossy data compression to climate model output is an attractive means of reducing the enormous volumes of data generated by climate models. However, because lossy data compression does not exactly preserve the original data, its application to scientific data must be done judiciously. To this end, a collection of measures is being developed to evaluate various aspects of lossy compression quality on climate model output. Given the importance of data visualization to climate scientists interacting with model output, any suite of measures must include a means of assessing whether images generated from the compressed model data are noticeably different from images based on the original model data. Therefore, in this work we conduct a forced‐choice visual evaluation study with climate model data that surveyed more than one hundred participants with domain relevant expertise. In addition to the images created from unaltered climate model data, study images are generated from model data that is subjected to two different types of lossy compression approaches and multiple levels (amounts) of compression. Study participants indicate whether a visual difference can be seen, with respect to the reference image, due to lossy compression effects. We assess the relationship between the perceptual scores from the user study to a number of common (full reference) image quality assessment (IQA) measures, and use statistical models to suggest appropriate measures and thresholds for evaluating lossily compressed climate data. We find the structural similarity index (SSIM) to perform the best, and our findings indicate that the threshold required for climate model data is much higher than previous findings in the literature.