基于DCT的分块自适应量化算法及其用于SAR原始数据压缩

该文提出了一种基于离散余弦变换(DCT)和分块自适应量化相结合的SAR原始数据压缩算法。利用SAR原始数据满足局部平稳高斯随机过程的特点,通过将DCT系数进行重排,并对重排后的系数矩阵进行有效的量化比特分配和分块自适应量化,从而大幅度提高了量化增益。通过对真实SAR原始数据的压缩实验结果表明：该文算法与BAQ算法相比,以相对较低的运算复杂度增加,使图像域的压缩性能指标有了明显提高。     

利用DCT变换和分段量化的成像声纳实时处理系统图像数据压缩技术研究

成像声纳实时处理系统在某些应用场合下传输图像数据时需要进行压缩,如搭载成像声纳的半潜式航行器与母船之间无线传输声纳数据。针对该应用需求,提出了利用离散余弦变换(DCT)和分段量化的声纳图像数据压缩技术。该方法处理的对象是成像声纳实时获取的图像行数据。方法的实施步骤为,首先对行数据进行DCT变换,其次利用DCT的能量集中特性,对变换后的DCT系数进行截断处理,最后对截断数据进行分段量化处理,进一步提高压缩效率。在显控端利用相逆的过程进行解压处理,实现成像结果的实时显示。对实际的声纳图像数据进行了处理,验证了方法的有效性。      

自适应量化表的JPEG压缩技术

本文在讨论ＪＰＥＧ标准压缩过程的基础上,提出了确定量化表的自适应方法并进行了图像实际压缩实验,证明了该技术的有效性和可行性。     

第四类DCT的快速算法

本文简要介绍了四类离散余弦变换,以第二类离散余弦变换的快速算法为基础,从公式上推出了第四类离散余弦变换的快速算法,在运算次数上与直接计算进行了比较,并给出了相应的Ｃ程序。     

基于JPEG2000的遥感图像分级量化压缩

介绍了JPEG2000静态图像压缩标准的基本情况;对遥感图像的相关性以及小波变换域统计特性进行了分析,得出遥感图像的特点。针对其特点,设计了基于JPEG2000无损压缩的高频子带分级量化高保真压缩方案。实验结果表明,此方案在对遥感图像进行压缩时取得了较好的效果。     

基于DCT的自适应多重彩色图像盲水印算法

针对现有图像水印算法的一些不足,提出了一种基于离散余弦变换的自适应多重彩色图像盲水印算法。该算法根据彩色载体图像内容特点,将预处理后的不同二值图像水印自适应地嵌入到其绿色分量的离散余弦变换低频系数和中频系数,实现了不同水印在彩色载体图像中的嵌入及盲提取。实验结果表明,该算法在保证不可觉察性的前提下能够较好地抵抗各类常规攻击,具有良好的稳健性。     

基于图像压缩量化系数的自适应水印算法

提出了一种基于图像压缩系数量化的自适应数字图像水印算法。该算法利用图像压缩中量化系数的特性,在其中嵌入水印。为增强水印的安全性用LFSR将置乱后的二值水印图像量化嵌入到载体图像的系数中,水印检测过程不需要原始图像的参与。实验结果表明,该算法不仅具有良好的不可见性,而且对常见攻击如JPEG压缩、噪声、滤波和几何攻击等具有较强的稳健性。     

二维DCT的基图像及其绘制

基图像是图像正交变换中比较重要的概念，但目前的文献对基图像的解释不够清楚，对基图像的绘制也没有提及。以二维DCT为例对基图像的概念和意义进行了详细地阐述；分析、解释了基图像的绘制原理，提出了一种用Matlab绘制二维DCT基图像的简便方法，该方法也适用于绘制其他正交变换的基图像。     

DCT快速算法及其VLSI实现

现在离散余弦变换（DCT）发展很快，本文概述了DCT的各种快速算法及其发展，将DCT算法进行了分类。文中详细地综述了适合于VLSI实现的各种DCT算法结构，并对这一领域的发展及应用前景进行了探讨。     

基于DCT域的菱形编码图像隐写改进算法

为提高嵌入秘密图像的信息量和嵌入后载体图像的质量,在行程编码、菱形编码和DCT域的基础上,提出了基于DCT域的菱形编码图像隐写改进算法.采用菱形编码有效地提高了嵌入率,对JPEG标准化量化表进行了改进,更有利于秘密信息的嵌入.对行程编码进行了改进,使秘密图像的压缩效率更大.经过与F5隐写算法对比实验表明,F5隐写算法只能嵌入一幅尺寸为64×64的灰度图像,而改进算法能够嵌入一幅尺寸为240×240的灰度图像,且PSNR＞ 30 dB,大大提高了加密信息的嵌入量,并保持了嵌入后载体图像的质量.     

A new LBG-based image compression method using DCT

In this letter, a new Linde-Buzo-Gray （LBG）-based image compression method using Discrete Cosine Transform （DCT） and Vector Quantization （VQ） is proposed. A gray-level image is firstly decomposed into blocks, then each block is subsequently encoded by a 2D DCT coding scheme. The dimension of vectors as the input of a generalized VQ scheme is reduced. The time of encoding by a generalized VQ is reduced with the introduction of DCT process. The experimental results demonstrate the efficiency of the proposed method.     

An Improved All Zero Block Finding method based on the statistic characteristics of DCT transform coefficients

In the H.263 video codec related systems, motion estimation and Discrete Cosine Transform （DCT） have the most computational requirements. In order to reduce complexity of the encoder to dedicate more resources to other functions, according to the study of existing methods, an Improved All Zero Block Finding （IAZBF） method based on the statistic characteristics of DCT coefficients is proposed. Compared with existing methods, IAZBF improves the detecting efficiency by about 50% without importing too much extra computation requirement. Being computed with additions and shifts instead of complicated multiplications, IAZBF is of low computation complexity, especially for low-end processors. In addition, IAZBF upholds picture fidelity and remains compatible with the H.263 bitstream standard.     

A lossless compression algorithm of remote sensing image for space applications

A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio （CR） of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.     

基于MATLAB的DCT域数字水印技术实现

研究了基于离散余弦变换(DCT)的数字图像水印算法，并借助一种高效实用的编程工具MATLAB，编程使这种算法得以实现，实验结果表明，对于通常的压缩编码，该算法具有足够的稳健性。     

任意长离散余弦变换的快速递归算法

该文基于Clenshaw递归公式以及离散余弦自身的对称性提出任意长离散余弦变换(DCT)的一种并行递归快速算法,给出了该算法的滤波器实现结构;与现有的其它递归算法以及基于算术傅里叶变换的余弦变换算法进行了计算复杂度的比较分析,结果表明该文算法运算量大大减少。该递归计算的滤波器结构使算法非常适合大规模集成电路(VLSI)实现。     

基于子集划分的素长度二维DCT快速算法

该文针对素长度类型的2维离散余弦变换(DCT)变换,提出一种子集划分准则,并根据该准则将2维DCT变换输出的频域数据集合划分为若干个互不相交子集;将对频域的计算转换为对2(N－1)个N点1维素数尺寸DCT的奇系数或偶系数的计算;最后给出了该算法的乘法复杂度和加法运算复杂度。相对于行列分解法,该算法节省了约一半的乘法次数,省略了数据的转置存储过程,而加法的运算复杂度基本维持不变。     

大容量多通道数字水印算法

现有许多数字水印算法基本上都是针对灰度图像的,彩色图像数字水印算法尚未得到充分研究,且所能嵌入水印的容量也不够大。本文提出的大容量多通道数字水印算法对这一问题进行了研究。该算法以彩色图像作为原始载体,通过数字水印压缩编码,载体图像颜色空间转换,彩色分量分块离散余弦变换,结合人眼视觉系统确定水印嵌入位置等措施,将二维水印图像嵌入到原始彩色载体图像中,且能嵌入较大容量的水印图像。实验结果表明,该算法不仅提高了水印容量,且对剪切、模糊、锐化等有损攻击具有良好的健壮性。     

基于功率谱离散余弦变换的BPSK信号参数估值

该文针对低信噪比环境下二相编码(BPSK)信号参数估值问题,提出一种基于功率谱离散余弦变换(DCT)的BPSK信号参数估值方法。该方法利用DCT的能量集中特性,通过对提取到的BPSK信号功率谱进行离散余弦变换(DCT)和阈值处理可以得到BPSK信号的码长估计。再进行逆离散余弦变换,可以进一步实现对BPSK信号功率谱的降噪处理,消除噪声对估值的影响,进而利用功率谱特征实现对载频和子脉冲宽度的准确估计。实验表明,该方法在低信噪比环境下,可以准确地识别出BPSK信号的码长和对BPSK信号载频和子脉冲宽度的精确估计,并在信噪比时,较对比方法载频和子脉冲宽度的估值准确率分别提高了22.1%和28.3%。     

A novel convolutional neural network architecture of multispectral remote sensing images for automatic material classification

For real-world simulation, terrain models must combine various types of information on material and texture in terrain reconstruction for the three-dimensional numerical simulation of terrain. However, the construction of such models using the conventional method often involves high costs in both manpower and time. Therefore, this study used a convolutional neural network (CNN) architecture to classify material in multispectral remote sensing images to simplify the construction of future models. Visible light (i.e., RGB), near infrared (NIR), normalized difference vegetation index (NDVI), and digital surface model (DSM) images were examined.This paper proposes the use of the robust U-Net (RUNet) model, which integrates multiple CNN architectures, for material classification. This model, which is based on an improved U-Net architecture combined with the shortcut connections in the ResNet model, preserves the features of shallow network extraction. The architecture is divided into an encoding layer and a decoding layer. The encoding layer comprises 10 convolutional layers and 4 pooling layers. The decoding layer contains four upsampling layers, eight convolutional layers, and one classification convolutional layer. The material classification process in this study involved the training and testing of the RUNet model. Because of the large size of remote sensing images, the training process randomly cuts subimages of the same size from the training set and then inputs them into the RUNet model for training. To consider the spatial information of the material, the test process cuts multiple test subimages from the test set through mirror padding and overlapping cropping; RUNet then classifies the subimages. Finally, it merges the subimage classification results back into the original test image.The aerial image labeling dataset of the National Institute for Research in Digital Science and Technology (Inria, abbreviated from the French Institut national de recherche en sciences et technologies du numérique) was used as well as its configured dataset (called Inria-2) and a dataset from the International Society for Photogrammetry and Remote Sensing (ISPRS). Material classification was performed with RUNet. Moreover, the effects of the mirror padding and overlapping cropping were analyzed, as were the impacts of subimage size on classification performance. The Inria dataset achieved the optimal results; after the morphological optimization of RUNet, the overall intersection over union (IoU) and classification accuracy reached 70.82% and 95.66%, respectively. Regarding the Inria-2 dataset, the IoU and accuracy were 75.5% and 95.71%, respectively, after classification refinement. Although the overall IoU and accuracy were 0.46% and 0.04% lower than those of the improved fully convolutional network, the training time of the RUNet model was approximately 10.6 h shorter. In the ISPRS dataset experiment, the overall accuracy of the combined multispectral, NDVI, and DSM images reached 89.71%, surpassing that of the RGB images. NIR and DSM provide more information on material features, reducing the likelihood of misclassification caused by similar features (e.g., in color, shape, or texture) in RGB images. Overall, RUNet outperformed the other models in the material classification of remote sensing images. The present findings indicate that it has potential for application in land use monitoring and disaster assessment as well as in model construction for simulation systems.     

Adaptive reconstruction technique for the lost information of the rectangular image area

The adaptive reconstruction for the lost information of the rectangular image area is very important for the robust transmission and restoration of the image. In this paper, a new reconstruction method based on the Discrete Cosine Transform (DCT) domain has been put forward. According to the low pass character of the human visual system and the energy distribution of the DCT coefficients on the rectangular boundary, the DCT coefficients of the rectangular image area are adaptively selected and recovered. After the Inverse Discrete Cosine Transform (IDCT), the lost information of the rectangular image area can be reconstructed. The experiments have demonstrated that the subjective and objective qualities of the reconstructed images are enhanced greatly than before.