Description: 这个是有关于量化的具体介绍是英文版的,介绍的很详细-this is on the quantification of specific details in English, introduced in great detail Platform: |
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Author:liyang |
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Description: 基于标量量化的图像多描述编码（MDSQ）是提高信息传输质量的一种有效方法,本程序利用Matlab 工具在处理矩阵上的灵活性,实现了MDSQ 算法, 并通过解码后的图像证明该方法良好的多描述性能。-Scalar quantization-based image multiple description coding (MDSQ) is to improve the quality of information transmission of an effective method, the procedures for the use of Matlab tool flexibility matrix treatment, realized MDSQ algorithm, and through decoded images show that the method good performance of multiple description. Platform: |
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Author:shanyingying |
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Description: L3_1.m: 純量量化器的設計(程式)
L3_2.m: 量化造成的假輪廓(程式)
L3_3.m: 向量量化器之碼簿的產生(程式)
L3_4.m: 利用LBG訓練三個不同大小與維度的碼簿並分別進行VQ(程式)
gau.m: ML量化器設計中分母的計算式(函式)
gau1.m: ML量化器設計中分子的計算式(函式)
LBG.m: LBG訓練法(函式)
quantize.m:高斯機率密度函數的非均勻量化(函式)
VQ.m: 向量量化(函式)
L3_2.bmp: 影像檔
lena.mat: Matlab的矩陣變數檔
-L3_1.m: scalar quantizer design (the program) L3_2.m: quantitative result of the false contour (the program) L3_3.m: Vector quantizer code book of the generation (the program) L3_4.m: training in the use of LBG three different size and dimension of the code book and separate VQ (program) gau.m: ML quantizer design in the denominator of the calculation formula (function) gau1.m: ML quantizer design in the calculation of molecule-type ( function) LBG.m: LBG training method (function) quantize.m: Gaussian probability density function of the non-uniform quantization (function) VQ.m: vector quantization (function) L3_2.bmp: image file lena.mat: Matlab matrix variable file Platform: |
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Author:Oki |
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Description: 编程实现基于8×8子块的DCT图像变换，基于JPEG量化矩阵的量化与反量化，基于8×8子块DCT的图像重建；以一幅512×512、8比特/象素的图像为实验对象，计算重建后的PSNR。-Programming based on 8 × 8 sub-block of the DCT image transform, quantization matrix based on the JPEG quantization and anti-quantitative, based on the 8 × 8 sub-block DCT of the image reconstruction to a 512 × 512,8 bits/as Su-image for the experiment, re-calculation of PSNR. Platform: |
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Author:zfyplay |
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Description: this a matlab function that can be given to the genetic algorithm tool box to chose the best 10 positions of 1 s beside the original 6 in the upper left corner 1 s in the dct quantization matrix -this is a matlab function that can be given to the genetic algorithm tool box to chose the best 10 positions of 1 s beside the original 6 in the upper left corner 1 s in the dct quantization matrix Platform: |
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Author:koko |
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Description: 对JPEG的压缩标准进行了仿真，主要过程DCT压缩，根据JPEG的量化矩阵对DCT结果进行量化，对量化结果进行Huffman编码。同时也实现了解压缩过程，即执行上述过程的逆过程。-JPEG compression standard of the simulation, the main process of DCT compression, according to the JPEG quantization matrix to quantify the results of the DCT, to quantify the results of Huffman coding. At the same time compression process to achieve understanding, that is, the inverse process of the implementation of the process. Platform: |
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Author:Justin |
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Description: （1）试利用函数dct2，对该图像进行压缩。压缩时可尝试对DCT 变换后的系数采用不同取舍方法，比较其压缩性能。（函数dct2在matlab本身是有的，可以help dct2看看）
（2）结合课本例8.7.2 的量化方法，对每一个经DCT变换后的8×8 矩阵量化（Huffman编码部分可省略），从而实现图像压缩。在一定压缩比的情况下，和⑴给出的图像质量相比较。
（3）用所给的子子程序fast_LOT.m 实现对该图像的变换与压缩，并和（1）得出的图像质量相比较-(1) Test using function dct2, the image is compressed. The compression can try to adopt different choices, the DCT transform coefficients after comparing its compression performance. (Function dct2 the matlab itself is some help dct2 see) (2) combined with the textbook cases 8.7.2 of quantitative methods, 88 each DCT transform matrix quantization (Huffman encoding part can be omitted) thereby achieving the image compression. In the case of a certain compression ratio, and the image quality given by the ⑴ compared. (3) with the given sub-subroutine fast_LOT.m transform and compress the image, and (1) gives the image quality compared Platform: |
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Author:zrf |
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Description: GLCM_Features1 helps to calculate the features from the different GLCMs
that are input to the function. The GLCMs are stored in a i x j x n
matrix, where n is the number of GLCMs calculated usually due to the
different orientation and displacements used in the algorithm. Usually
the values i and j are equal to NumLevels parameter of the GLCM
computing function graycomatrix(). Note that matlab quantization values
belong to the set {1,..., NumLevels} and not from {0,...,(NumLevels-1)}
-GLCM_Features1 helps to calculate the features from the different GLCMs
that are input to the function. The GLCMs are stored in a i x j x n
matrix, where n is the number of GLCMs calculated usually due to the
different orientation and displacements used in the algorithm. Usually
the values i and j are equal to NumLevels parameter of the GLCM
computing function graycomatrix(). Note that matlab quantization values
belong to the set {1,..., NumLevels} and not from {0,...,(NumLevels-1)}
Platform: |
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Author:almosawi |
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Description: glcm mean in matlab by armin moghimi devloped GLCM_Features1 helps to calculate the features from the different GLCMs
that are input to the function. The GLCMs are stored in a i x j x n
matrix, where n is the number of GLCMs calculated usually due to the
different orientation and displacements used in the algorithm. Usually
the values i and j are equal to NumLevels parameter of the GLCM
computing function graycomatrix(). Note that matlab quantization values
belong to the set {1,..., NumLevels} and not from {0,...,(NumLevels-1)}
as provided in some references
http://www.mathworks.com/access/helpdesk/help/toolbox/images/graycomatrix
.html-glcm mean in matlab by armin moghimi devloped GLCM_Features1 helps to calculate the features from the different GLCMs
that are input to the function. The GLCMs are stored in a i x j x n
matrix, where n is the number of GLCMs calculated usually due to the
different orientation and displacements used in the algorithm. Usually
the values i and j are equal to NumLevels parameter of the GLCM
computing function graycomatrix(). Note that matlab quantization values
belong to the set {1,..., NumLevels} and not from {0,...,(NumLevels-1)}
as provided in some references
http://www.mathworks.com/access/helpdesk/help/toolbox/images/graycomatrix
.html Platform: |
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Author:armin |
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Description: matlab编写的图像边缘直方图提取程序，最后结果包含了归一化边缘梯度直方图（矩阵第-行），相应量化区间均值（矩阵第二行），相应量化区间均方差（第三行），具体见从程序，有注释-write histogram matlab image edge extraction procedures, the final result contains a normalized edge gradient histogram (Matrix- line), corresponding quantization interval mean (matrix second row), corresponding quantization interval mean square error (third row), see in particular from the program, there is a comment Platform: |
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Author:吴亮 |
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Description: 理解使用向量量化进行图像量化的原理：对序列中的许多样本进行联合量化，用一个值代替相似的一组值，减少量化误差；
掌握向量量化器码书设计的方法，此次实验使用LBG算法设计码书；
3. 对LBG算法的理解和使用；
4.Matlab矩阵数据的处理。-Understood that the use of vector quantization for image quantized principle: a number of samples of the sequence are jointly quantified, instead of a similar set of values with a value, to reduce the quantization error master Vector quantizer codebook design, the experimental use LBG algorithm codebook design 3. LBG algorithm to understand and use 4.Matlab matrix data processing. Platform: |
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Author:黎鸿朗 |
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Description: 灰度共生矩阵建立在估计图像的一阶组合条件概率密度函数的基础上, 其通过计算图像中有一定距离和一定方向的两点之间灰度的相关性, 反映图像在方向、间隔、变化幅度及快慢上的综合信息。-The GLCMs are stored in a i x j x n matrix, where n is the number of GLCMs calculated usually due to the different orientation and displacements used in the algorithm. Usually the values i and j are equal to NumLevels parameter of the GLCM computing function graycomatrix(). Note that matlab quantization values belong to the set {1,..., NumLevels} and not {0,...,(NumLevels-1)} as provided in some references Platform: |
Size: 1024 |
Author:宋超 |
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Search - matlab matrix quantization