Description: 本文结合DCT变换的特性，利用分形的Peano一hilbert扫描曲线，对基于DCT变换的图像编码方法进行了研究，并提出了改进方法。主要工作分为两个部分: 一是在分块DCT基础上的改进。对分块后生成的各图像块矩阵，不直接作二维DCT变换，而是先进行扫描，使之重排成一维向量，再进行一维DCT变换，这样可以减少运算量。 其次是对层次余弦变换编码部分的改进。使用层次DCT变换与零树编码相结合的方法，并在扫描编码过程中，按Peano一hilbert扫描顺序进行编码。这样不仅可以减少“块效应”，而且可以加快编码速度。-this paper the characteristics of DCT, using fractal Peano a Hilbert scanning curve, the DCT-based image coding method for the study and improvement of methods. The work is divided into two main parts : First, the DCT block on the basis of improvements. Right after block production of the image block matrix, not for direct two-dimensional DCT, but first scan, so they line up in a heavy-dimensional vector and then a peacekeeping DCT, thus reducing the amount of computation. Secondly, the level of cosine transform coding some of the improvements. The use level of DCT and zero tree coding combination of methods, coding and scanning process, according to a Hilbert Peano code scanning sequence. This will not only reduce the "blocking effect" but we can speed up the coding speed. Platform: |
Size: 5011456 |
Author:lzy |
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Description: 余弦变换压缩，用于图像压缩，压缩比高，且压缩后图像质量仍然很好。适用于灰度图象。-Cosine Transform compression for image compression, compression ratio, and compressed image quality is still good. Apply to the gray-scale image. Platform: |
Size: 34816 |
Author:张弛 |
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Description: dct and idct for image.matlab实现信息序列的嵌入和提取-dct and idct for image.matlab realize embedded information and extract sequence Platform: |
Size: 2048 |
Author:wubaishan |
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Description: 使用二维离散余弦变换(DCT)进行图象压缩。原始图象被转换成为8*8大小的块进行压缩和重构。 Cnum:为压缩时保留的系数的个数 MSM:均方误差压缩应该做到在最合理的近似原图象的情况下使用最少的系数。函数：dctmtx:返回一个n*n阶DCT变换 blkproc:对图象进行不同的块处理。 im2col:重排图象块为矩阵列。注意：只适用于灰度图象。-The use of two-dimensional discrete cosine transform (DCT) for image compression. Original image is converted to become the size of 8* 8 block compression and reconstruction. Cnum: reservations for the compression factor of the number of MSM: mean square error compression should be done in the most reasonable approximation of the original image using the least coefficient. Function: dctmtx: returns an n* n-order DCT transform blkproc: image processing of different blocks. im2col: rearrangement of the matrix block out images. Note: only applicable to grayscale images. Platform: |
Size: 3072 |
Author:hff |
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Description: 基于DCT域的图像数字水印技术：
摘要：数字水印技术作为数字媒体版权保护的有效办法，近年来在国内外引起了人们极大的兴趣。作者采用了DCT离散余弦变换的算法，对图像这种多媒体数字作品进行了数字水印的嵌入和检测研究。并且使用了高效实用的编程工具MATLAB来介绍了一种DCT域的图像数字水印嵌入和提取的实例，并且在最后对嵌入水印的图像进行了攻击实验，验证了该算法的鲁棒性和不可见性。-Based on the DCT domain image watermarking techniques: Abstract: Digital watermarking of digital media technology as an effective means of copyright protection, in recent years both at home and abroad has aroused great interest. The author adopted the DCT discrete cosine transform algorithms, the image of this multi-media digital works for the digital watermark embedding and detection research. And the use of efficient MATLAB programming tools useful to introduce a DCT domain image watermarking embedding and extraction examples, and in the final of the embedded watermark image of the attack experiments verify the robustness of the algorithm and non- Visibility. Platform: |
Size: 533504 |
Author:绯村剑心 |
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Description: 在图像的dct域内嵌入数字水印，包括水印嵌入和提取程序。水印为一副图像。-In the image embedded in the DCT domain of digital watermarking, including the watermark embedding and extraction procedures. Watermark for an image. Platform: |
Size: 1024 |
Author:霞疙瘩228 |
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Description: 这是用matlab写的dct代码用于图像的信息的隐藏和提取。。部分老师写的哦。-This is used to write the DCT matlab code for the image and extract the hidden information. . Oh part of the Writing Teacher. Platform: |
Size: 1024 |
Author:chenlihua |
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Description: 图像变换 将图象分块(8*8)，作DCT变换，并作IDCT：DCT是一种可分离图象变换，用来图象压缩编码，IDCT用来解压缩-Image transform to image sub-block (8* 8), for the DCT transform, and IDCT: DCT is a separable transform image to image compression coding, IDCT used to extract Platform: |
Size: 17408 |
Author:陈杰 |
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Description: 已知两个不同图像块亮度数据如下：
（1）分析DCT原理，采用DCT方法，编程并计算相应的DCT系数，分析系数分布特点。
（2）依据视觉特性分析量化表步长的分布特点，完成DCT系数量化。
（3）采用Z形扫描，实现输出数据的统计编码，形成Video stream。
（4）采用IDCT重建图像亮度数据，计算SAD大小，分析产生误差的原因及采用DCT进行数据压缩的原理。（ ）
（5）分别利用左上角1、3、6个系数重建图像，计算相应的SAD，并由此分析直流和低频系数的重要性。
-Known brightness of two different image data block is as follows: (1) Principles of analysis of DCT, the DCT method, program and calculate the corresponding DCT coefficient, analysis of distribution coefficients. (2) quantitative analysis based on visual characteristics of the distribution of long-form features step-by-step to complete the DCT coefficient quantization. (3) the use of Z-scan output data to achieve statistical encoding, the formation of Video stream. (4) the use of the reconstructed image brightness IDCT data to calculate the SAD size, analysis of the causes of errors and the use of DCT for the principle of data compression. () (5), respectively, a factor of 1,3,6 using the upper left corner of the reconstruction of images, calculating the corresponding SAD, and the resulting analysis of DC and the importance of low-frequency coefficients. Platform: |
Size: 1024 |
Author:张元 |
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Description: 水印提取检测的matlab程序代码
对嵌入水印图像进行dct变化，提取水印序列，计算序列相关值-Detection of watermark extraction matlab code for dct embedded watermark image change, extract the watermark sequence, calculating the value of serial correlation Platform: |
Size: 3072 |
Author:顾利雯 |
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Description: Matlab source code for DCT-based watermarking for grayscale images. DCT-based watermark recovering without resorting to the uncorrupted original image. Digital watermarking has been proposed as a viable solution to the need of copyright protection and authentication of multimedia data in a networked environment, since it makes possible to identify the author, owner, distributor or authorized consumer of a document.
A new watermarking technique to add a code to digital images is presented: the method operates in the frequency domain embedding a pseudo-random sequence of real numbers in a selected set of DCT coefficients. Watermark casting is performed by exploiting the masking characteristics of the Human Visual System, to ensure watermark invisibility. The embedded sequence is extracted without resorting to the original image, so that the proposed technique represents a major improvement to methods relying on the comparison between the watermarked and original images.
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Author:omar |
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Description: This a JPEG encoding & decoding program of still image. It does not use level shifting. Discrete Cosine transform (DCT) is performed both by classical & Chen s Flowgraph methods. Predefined JPEG quantization array & zigzag order are used here. RUN - LEVEL coding is used instead of Huffman coding. Compression ratio is compared for each DCT method. Effect of coarse and fine quantization is also examined. The execution time of 2 DCT methods is also checked. In addition, most energatic DCT coefficients are also applied to examine the effect in MatLab 7.4.0 R2007a. Input is 9 gray scale pictures & output is 9*9=81 pictures to compare. Blocking effect is obvious.
-This is a JPEG encoding & decoding program of still image. It does not use level shifting. Discrete Cosine transform (DCT) is performed both by classical & Chen s Flowgraph methods. Predefined JPEG quantization array & zigzag order are used here. RUN - LEVEL coding is used instead of Huffman coding. Compression ratio is compared for each DCT method. Effect of coarse and fine quantization is also examined. The execution time of 2 DCT methods is also checked. In addition, most energatic DCT coefficients are also applied to examine the effect in MatLab 7.4.0 R2007a. Input is 9 gray scale pictures & output is 9*9=81 pictures to compare. Blocking effect is obvious.
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Size: 95232 |
Author:Ho Joon Lee |
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Description: 本文设计基于DCT的人脸识别系统，首先结合当今人脸识别的背景和发展状况讨论了人脸识别的研究内容及在各方面的应用；然后研究了人脸识别进行预处理，讨论了人脸识别预处理的其他方法，分析各种方法的利弊，最后采用DCT（离散余弦变换）实现人脸图像预处理中的降维处理；接下来对人脸图像的特征提取进行了研究，简单叙述了几何特征提取和代数特征提取，同时深入研究了基于DCT和PCA变换的人脸图像特征提取，从而实现是否对人脸识别系统识别率有所提高的研究；对于分类器的选择，本文对两种分类器进行了探讨，即最近邻分类器和BP神经网络分类器，同时采用BP神经网络分类器作为本次基于DCT的人脸识别系统设计的分类器，并对BP神经网络进行分类的算法进行设计，BP神经网络具有学习功能，只要采用本系统对训练图片进行训练，就可以记下图像的相关信息，对于测试图片，就可以很准确的识别出该图片是属于哪个的。最后，本文对整个人脸识别系统设计实验进行了实验分析，实验结果表明本文采用的方法切实有效。- This is the design of the DCT-based face recognition systems. First of all, the background light of the current face recognition and face recognition to discuss the development of research in all aspects of content and applications And then studied the pretreatment of face recognition to discuss the pre-treatment of other face recognition methods,and analysis of the pros and cons of various methods, finally, the use of DCT (discrete cosine transform) image pre-processing to achieve in the face of the reduced-order processing Next on the face image feature extraction have been studied, A brief description of the feature extraction and algebraic geometry feature extraction, while in-depth study based on the DCT and the PCA Transform face image feature extraction in order to achieve face recognition system to identify whether the rate of increase in the research For the choice of classifier, this paper carried out on two of classifier, that is, nearest neighbor classifier and BP neura Platform: |
Size: 422912 |
Author:刘文珍 |
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Description: High information redundancy and correlation in face images result in efficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.-High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches. Platform: |
Size: 25600 |
Author:mhm |
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