基于方向性小波子树分形图像编码的改进算法

本文在分析混合分形零树小波图像编码算法(FZW)优缺点的基础上，提出一种新的基于方向性小波子树的分形图像编码算法。该算法结合零树小波编码和分形编码，通过在匹配搜索过程中使用方向性range和domain子树，提高匹配精度，改善了传统分形小波图像压缩中的方块效应，更大限度的保留了图像的边缘信息。实验结果表明，该算法在提高压缩比和去除图像的方块效应方面，均取得了良好的效果。     

基于Mallat分解与Contourlet方向滤波的图像压缩

小波变换以其多分辨率分析和时频局域特性在信号处理中得到了广泛的应用.二维Mallat小波分解只能将图像分解为平滑、水平细节、垂直细节、对角线细节等四个部分.大多数图像特别是自然图像存在丰富的方向信息,然而Mallat分解只能对水平、垂直、对角线三个方向分析图像细节,所以在分解方向上存在不足.Contourlet变换除了具有和小波一样的多尺度、时频局部性,还具有方向性和各向异性.但是这种方法变换后数据量大于原始数据,存在冗余.将Mallat分解和Contourlet中的方向滤波相结合,提出了一种新的图像压缩方法,保持了二维Mallat分解的无冗余性,并且充分利用了Contourlet的方向性.仿真实验表明这种方法优于单独采用Mallat分解和Contourlet变换.     

基于图像处理的多小波变化理论及其应用

为了使图像处理更好地服务于信息的传递和表达,在此引入多小波变换理论。通过对多小波变换的原理进行分析,在结合离散多小波变换实现方法的基础上,对图像处理中多小波变换的应用展开了深入研究。研究表明,与单小波相比,多小波变换在图像压缩以及图像去噪等方面均具有更加显著的效果。     

基于非对称离散高斯似然的深度图像压缩方法

相较于传统的图像压缩技术,深度图像压缩可以提供更优的率失真性能,甚至可以超越最新的压缩编码标准多功能视频编码（Versatile Video Coding,VVC）。然而,随着网络复杂度的提升,深度图像压缩技术的提升亦有瓶颈。因此,提出了非对称离散高斯分布的深度图像压缩方法。并非优化编解码器或是熵模型,该方法在隐空间借助语义信息和稀疏过程,实现单高斯分布向非对称高斯分布的迁移,以节约码流。相较其他方法,所提方法具有更优的率失真性能,在Kodak数据集上解码的图像更加真实自然。     

基于人眼视觉特性的EZW图像编码改进算法

在经典EZW(crnbedded zero-tree wavelet)算法的基础上,根据人眼对图像边缘和平滑区信息的失真要比纹理区失真更敏感这一视觉特性,对小波变换后的系数分类编码,提出了一种新的有效的图像压缩编码算法.实验结果表明由于边缘和极平滑区小波系数分配了更多的比特数,该算法所得的重构图像边缘更清晰,具有更好的视觉效果.     

DCT图像压缩及LabVIEW实现

图像压缩有助于图像的大量存储以及快速实时地传输信息,在当下科技时代有着广泛的应用.为了实现图像压缩,采用LabVIEW软件开发平台,设计了一个以离散余弦变换为理论基础的图像压缩系统,该系统可以通过不同的模式设置来控制压缩图像的性质,并且在操作面板上显示离散余弦变换的系数以及压缩后的图像,具有开发时间短、成本低、编程易懂、维护简单等优点,无论在工业领域还是在日常生活中都具有应用价值,取得了较理想的效果.     

基于D9/7小波变换的自适应方向提升多小波算法

提出一种自适应方向提升多小波的算法,结合多小波变换的对称性、正交性、紧支性、高阶消失矩以及自适应算法的方向性特征,提高图像压缩的性能。传统的多小波变换只考虑水平与垂直两个方向上的变换,而自适应提升多小波利用图像局部的方向特征寻找像素点之间的相关性,从而减少编码时的预测误差。利用两个D9/7单小波构造提升多小波,并结合率失真(RD)模型自适应地进行提升方向的选择,在减少预测误差与编码比特率之间进行折中选择。实验结果表明,自适应方向提升多小波比传统的提升多小波具有更好的编码性能。     

基于平滑阈值函数的Ridgelet图像去噪

Ridgelet变换是继小波(Wavelet)变换后提出的一种新型的多尺度分析方法。它不仅具有小波变换的优点,还能更有效地表示图像的线性性质。鉴于Ridgelet变换具有多方向性,能够更好地恢复图像的边缘信息,得到比小波去噪更好的效果。本文将有限Ridgelet变换结合平滑阈值函数应用到特定图像的去噪中,试验结果表明,它比小波去噪及基于Ridgelet变换的简单阈值去噪效果更好。     

基于DCT的图像压缩及MATLAB实现

介绍JPEG图像压缩算法,并在MATIAB数学分析工具环境下从实验角度出发,较为直观地探讨了DCT在JPEG图像压缩中的应用。仿真实验表明,用MATLAB来实现离散余弦变换的图像压缩,具有方法简单、速度快、误差小的优点,大大提高了图像压缩的效率和精度。     

基于MATLAB的DCT变换在JPEG图像压缩中的应用

介绍了JPEG图像压缩算法,并在MATLAB数学分析工具环境下从实验角度出发,较为直观地探讨了DCT在JPEG图像压缩中的应用。仿真实验表明,用MATLAB来实现离散余弦变换的图像压缩,具有方法简单、速度快、误差小的优点,大大提高了图像压缩的效率和精度。     

Synthetic aperture radar image compression using tree-structured edge-directed orthogonal wavelet packet transform

Currently, wavelet-based coding algorithms are popular for synthetic aperture radar (SAR) image compression, which is very important for reducing the cost of data storage and transmission in relatively slow channels. However, standard wavelet transform is limited by spatial isotropy of its basis functions that is not completely adapted to represent image entities like edges or textures, which means wavelet-based coding algorithms are suboptimal to image compression. In this paper, a novel tree-structured edge-directed orthogonal wavelet packet transform is proposed for SAR image compression. Inspired by the intrinsic geometric structure of images, the new transform improves the performance of standard wavelet by filtering along the regular direction first and then along the orthogonal direction with directional lifting structure. The cost function of best basis selection is designed by textural and directional information for tree-structured edge-directed orthogonal wavelet packet transform. The new transform including speckle reduction can be used to construct SAR image coder with the embedded block coding with optimal truncation for transform coefficients, and arithmetic coding for additional information. The experimental results show that the proposed approach outperforms JPEG2000 and Fast wavelet packet (FWP), both visually and item of PSNR values.     

基于量化的双树复小波变换鲁棒图像水印算法

Conventional quantization index modulation (QIM) watermarking uses the fixed quantization step size for the host signal. This scheme is not robust against geometric distortions and may lead to poor fidelity in some areas of content. Thus, we proposed a quantization-based image watermarking in the dual tree complex wavelet domain. We took advantages of the dual tree complex wavelets (perfect reconstruction,approximate shift invariance, and directional selectivity). For the case of watermark detecting, the probability of false alarm and probability of false negative were exploited and verified by simulation.Experimental results demonstrate that the proposed method is robust against JPEG compression, additive white Gaussian noise (AWGN), and some kinds of geometric attacks such as scaling, rotation, etc.     

Image sequence coding using oriented edges

Second generation image coding techniques, which use information about the human visual system to reach high compression ratios, have proven very successful when applied to single images. These methods can also be applied to image sequences. A directional decomposition based sequence coding technique is presented, in which spatial lowpass and highpass components are analyzed and coded separately. A simple law for sharing the available bits between these components is stated and analytically proved by using a minimum cost/resolution optimality criterion. The detection of directional elements is carried out by using both linear and nonlinear (median) filtering. The coding is based on near optimal estimators which retain only the innovation part of information, and is well suited for differential pulse code modulation. The results of applying this method to a typical sequence are shown. The estimated compression ratio is approximately 320 : 1 (0.025 bits per pixel), allowing a transmission rate of about 41 kbit/second. The resulting image quality is reasonably good.     

有损视频传输中的方向性结构与纹理重建

当被基于宏块预测的压缩方法压缩的视频码流通过有损信道传输的时候,丢包现象会导致错误沿着时域预测而传递.文中提出了一种有别于传统误差掩盖方法的,利用相临区域与未知区域的相关性来重构未知区域的方法.为了重构出有着较为复杂结构的未知区域,提出了用基于结构的方向插值来恢复结构信息周围的信息.而后,用基于纹理方向的Graph Cuts基准块排放方式来重构大块的纹理结构.为了进一步提高重建结果的质量,引入了重构顺序补偿.就像在实验结果中呈现的那样,提出的重建算法对于有着复杂结构的信息重建有着较好的作用.     

匹配视觉生理机制的方向分解图象编码

本文对方向分解图像编码进行了系统研究。与Kunt方案相比,提出了更有效的边缘信息选择和编码算法,改进了内插方法和模型,并采用了新的方向滤波器设计方法,基于上述改进,建立了一套完整的匹配视觉生理机制的方向分解图像编码算法。系统模拟实验表明,当数据压缩比近60:1时,采用本方案仍能获得较好的图像质量。     

基于多尺度几何分析的复杂网络压缩策略

自然界许多系统都具有多尺度效应,人们对系统的观察或测量也往往是在不同尺度上进行的。复杂网络可视为描述复杂系统的抽象模型,多尺度几何分析提供一种有效刻画复杂系统结构由粗到细的显微镜策略。该文从多尺度几何分析的角度对复杂网络压缩问题进行了有益的尝试,提出一种有效的网络压缩策略。策略分析及通过对不同拓扑类型的网络仿真表明基于多尺度几何分析的复杂网络压缩策略是可行的,能有效地帮助人们通过尽可能少的信息来分析、刻画大型复杂网络,实现网络数据及结构的稀疏表示。     

Deep image compression based on multi-scale deformable convolution

Deep image compression efficiency has been improved in the past years. However, to fully exploit context information for compressing image objects of different scales and shapes, more adaptive geometric structure of inputs should be considered. In this paper, we novelly introduce deformable convolution and its spatial attention extension into deep image compression task to fully exploit the context information. Specifically, a novel deep image compression network with Multi-Scale Deformable Convolution and Spatial Attention, named MS-DCSA, is proposed to better extract compact and efficient latent representation as well as reconstruct higher-quality images. First, multi-scale deformable convolution is presented to provide multi-scale receptive fields for learning spatial sampling offsets in deformable operations. Subsequently, multi-scale deformable spatial attention module is developed to generate attention masks to re-weight extracted features according to their importance. In addition, the multi-scale deformable convolution is applied to design delicate up/down sampling modules. Extensive experiments demonstrate that the proposed MS-DCSA network achieves improved performance on both PSNR and MS-SSIM quality metrics, compared to conventional as well as competing deep image compression methods.     

Rapid Image Completion System Using Multiresolution Patch-Based Directional and Nondirectional Approaches

This study presents a rapid image completion system comprising a training (or analysis) process and an image completion (or synthesis) process. The proposed system adopts a multiresolution approach, which not only improves the convergence rate of the synthesis process, but also provides the ability to deal with large replaced regions. In the training process, a down-sampling approach is applied to create a patch-based texture eigenspace based on multiresolution background region information. In the image completion process, an up-sampling approach is applied to synthesize the replaced foreground regions. To ensure the continuity of the geometric texture structure between the original background scene regions and the replaced foreground regions, directional and nondirectional image completion approaches are developed to reconstruct the global geometric structure and to enhance the local detailed features of the replaced foreground regions in the lower and higher resolution level images, respectively. Moreover, the synthesis priority order of the individual patches and the appropriate choice of completion scheme (i.e., directional or nondirectional) are both determined in accordance with a Hessian matrix decision value (HMDV) parameter. Finally, a texture refinement process is performed to optimize the resolution of the synthesized result.      

Geometric model for DSPN satellite reception in the dense scatterermobile environment

A geometric propagation model is developed for simulating the reception of direct sequence pseudonoise (DSPN) satellite signals by a directional or omni directional antenna in the dense scatterer mobile environment. The model is used to predict fading statistics. As expected, the mitigation of fading is closely related to the ratio of DSPN chip duration to delay spread of the scatterer medium for both the omni and directional antennas. The results suggest a general, frequency domain interpretation for the value of DSPN in mitigating multipath fading     

A new video watermarking algorithm based on 1D DFT and Radon transform

In this paper, we propose a new video watermarking algorithm based on the 1D DFT (one-dimensional discrete Fourier transform) and Radon transform. The 1D DFT for a video sequence generates an ideal domain, in which the spatial information is still kept and the temporal information is obtained. With detailed analysis and calculation, we choose the frames with highest temporal frequencies to embed the fence-shaped watermark pattern in the Radon transform domain of the selected frames. The adaptive embedding strength for different locations keeps the fidelity of the watermarked video. The performance of the proposed algorithm is evaluated by video compression standard H.264 with three different bit rates; geometric attacks such as rotation, translation, and aspect-ratio changes; and other attacks like frame drop, frame swap, spatial filtering, noise addition, lighting change, and histogram equalization. The main contributions of this paper are the introduction of the 1D DFT along temporal direction for watermarking that enables the robustness against video compression, and the Radon transform-based watermark embedding and extraction that produces the robustness against geometric transformations. One of the most important advantages of this video watermarking algorithm is its simplicity and practicality.