AI检测代码解析 importtorchimportnumpyasnpdefdct_ii(x):N=x.size(0)factor=np.pi/N# 创建DCT变换矩阵dct_matrix=torch.empty(N,N)forkinrange(N):forninrange(N):dct_matrix[k,n]=np.cos(factor*(n+0.5)*k)# 进行DCT变换result=dct_matrix @ x result*=np.sqrt(2/N)result[0]*=1/np.sqrt(...
#define DCT_SIZE 8 /* defines the input matrix as 8x8 */ #define CONST_BITS 13 #define DESCALE(x,n) (((x) + (1 << ((n)-1))) >> n) void dct(short input[N], short output[N]); #endif // __DCT_H__ not defined 至此,所有的源文件和头文件全部生成。对源文件进行综合(Synth...
numeric matrix DCT matrix, returned as a numeric matrix of sizen-by-n. Data Types:double Tips If you have ann-by-nimage,A, thenD*Ais the DCT of the columns ofAandD'*Ais the inverse DCT of the columns ofA. The two-dimensional DCT ofAcan be computed asD*A*D'. This computation is...
% Form intermediate even-symmetric matrix y = zeros(2*n,m);y(1:n,= aa;y(n+1:2*n,= ...
17、2double(I1); %将原图像转为双精度数据类型imsize_x,imsize_y=size(input_image);%-figureimshow(input_image);title(原始图像);%-根据DCT系数优先级矩阵,选择DCT系数-coef_selection_matrix_8x8=zeros(8,8);chosen_number_of_dct_coefficient=21;for l=1:chosen_number_of_dct_coefficienty,x=find...
格式:D=dctmtx(n)说明:D=dctmtx(n)返回一个nn的DCT变换矩阵,输出矩阵D为double类型。4.图像去噪的实现简介MATLAB是矩阵实验室(Matrix Laboratory)的简称,在数学类科技应用应用广泛。MATLAB可以进行矩阵运算、绘制函数和数据、实现算法、创建用户界面、连接其他编程语言的程序等,主要应用于工程计算、控制设计、信号处理...
(u * scale)) # here use opencv to get rotation, better write yourself rotation function C1=cv2.getRotationMatrix2D((new_width/2.0, new_height/2.0), angle, 1) new_img=cv2.warpAffine(noise, C1, (int(new_width), int(new_height)), borderValue=0) offset_height = abs(new_height - ...
压缩算法中,先将输入图像划分为8×8或16×16的图像块,对每个图像块作DCT 变换;然后舍弃高频的系数,并对余下的系数进行量化以进一步减少数据量;最后使用无失真编码来完成压缩任务。解压缩时首先对每个图像块做DCT 反变换,然后将图像拼接成一副完整的图像。1.1 DCT 变换的定义 一维DCT 的变换核定义为 g (x ,...
A DCT matrix decomposing method and DCT device for decomposing a one−dimensional DCT matrix so that a DCT operation can be carried out through only addition and subtraction. The method is characterized by comprising a first step of decomposing an N×N one−dimensional DCT matrix into ...
# confusion matrix:每一行表示真实category,每一列表示预测的category confusion = torch.zeros(n_categories, n_categories) n_confusion = 10000 # Just return an output given a line def evaluate(line_tensor): hidden = rnn.initHidden() for i in range(line_tensor.size()[0]): ...