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: 实现图像处理中，实现对一幅灰度图像的快速傅立叶变换,并求其变换后的系数（幅度）分布，同时实现对一幅图像做离散余弦变换,选择适当的DCT系数阈值对其进行DCT反变换 -Realize image processing, to achieve the fast Fourier transformation of a grayscale image, and determine the distribution of its transform coefficients (amplitude), while achieving a discrete cosine transform of an image do, to select the appropriate threshold value of the DCT coefficients to the DCT inverse transform Platform: |
Size: 169984 |
Author:茗汀 |
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Description: 1.实现对lena.bmp灰度图像的快速傅立叶变换,并求其变换后的系数（幅度）分布；
2.实现对lena.bmp图像做离散余弦变换,选择适当的DCT系数阈值对其进行DCT反变换 ；-1 to achieve the fast Fourier transform on lena.bmp grayscale image, and to find the transformed coefficients (amplitude) distribution 2. To achieve lena.bmp images do discrete cosine transform, select the appropriate DCT coefficient threshold to the DCT inverse transform Platform: |
Size: 88064 |
Author:戴咪嘟 |
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Description: 传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这
一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT)
系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表
示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的
语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优
于传统的语音增强方法。-The spectral components of speech are usually assumed to be independent in traditional short-time
spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm
with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform
(DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral
components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed
model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech
presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show
that the developed method has better noise suppression performance and lower speech distortion compared to the
traditional speech enhancement method. Platform: |
Size: 1054720 |
Author:立枣酒 |
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Search - dct coefficients distribution