sw222.m

这是偶自己的作品拉

function wadec2()
X=imread('E:\matlab1\LENAcaise128.bmp','bmp');
figure(100);imshow(uint8(X));title('original image');

Wavelet_Name='db1';
Wavelet_Scale=1;

[LL1,LH1,HL1,HH1]=swt2(double(X(:,:,1)),Wavelet_Scale,Wavelet_Name);
[LL12,LH12,HL12,HH12]=swt2(LL1,1,Wavelet_Name);

%[C,S]=wavedec2(double(X(:,:,1)),Wavelet_Scale,Wavelet_Name);
%appcoef2用来提取二维信号小波分解的近似分量
%LL1=appcoef2(C,S,Wavelet_Name,Wavelet_Scale);
%detcoef2用来提取二维信号小波分解的细节分量
%D=detcoef2(O,C,S,N)，O指定细节分量的类型，
%当O=‘h’时，重构水平细节分量，当O=‘v’时，重构垂直细节分量，当O=‘d’时，重构细节细节分量
%LH1=detcoef2('v',C,S,1);
%HL1=detcoef2('h',C,S,1);
%HH1=detcoef2('d',C,S,1);

%训练LL1频带码本，将码本大小设为128，维度为4 %
th=0.05;
nc=128;nd=4;
[cbLL12]=LBG(LL12,nc,nd,th); %LBG.m在L3.3节中

%利用一层小波变换加上向量量化进行Lena图像压缩%
%输入一幅Lena图像%
X=imread('E:\matlab1\LENAcaise128.bmp','bmp');
%load lena
[M,N]=size(X(:,:,1));
%使用Matlab中Wavelet工具箱的周期性小波变换函数dwtper2.m%
%并使用双正交小波bior3.3进行一层小波变换%
%[LL1,LH1,HL1,HH1]=dwtper2(X,'bior3.3');
Wavelet_Name='db1';
Wavelet_Scale=1;
[LL1,LH1,HL1,HH1]=swt2(double(X(:,:,1)),Wavelet_Scale,Wavelet_Name);
[LL12,LH12,HL12,HH12]=swt2(LL1,1,Wavelet_Name);
%分别针对不同的频带进行向量量化，其中VQ.m置于L3.4节中%
[R_LL12,bitLL12]=VQ(LL12,cbLL12);

%计算图像经过向量量化后的压缩率CR及失真PSNR%
totalbit=bitLL12   %花费的总位数 28672
%Y=idwtper2(R_LL1,R_LH1,R_HL1,R_HH1,'bior3.3'); %反小波变换
%C1= [R_LL1 R_HL1 R_LH1 R_HH1];
%S1=S;
%Y1=waverec2(C1,S1,Wavelet_Name);
Y12=iswt2(R_LL12,HL12 ,LH12, HH12,'db1');
Y1=iswt2(Y12,HL1 ,LH1, HH1,'db1');



Wavelet_Name='db1';
Wavelet_Scale=1;
[LL2,LH2,HL2,HH2]=swt2(double(X(:,:,2)),Wavelet_Scale,Wavelet_Name);
[LL22,LH22,HL22,HH22]=swt2(double(X(:,:,2)),2,Wavelet_Name);

%appcoef2用来提取二维信号小波分解的近似分量
%训练LL1频带码本，将码本大小设为128，维度为4 %
th=0.05;
nc=128;nd=4;
[cbLL22]=LBG(LL22,nc,nd,th); %LBG.m在L3.3节中

%输入一幅Lena图像%
X=imread('E:\matlab1\LENAcaise128.bmp','bmp');
%load lena
[M2,N2]=size(X(:,:,2));
%使用Matlab中Wavelet工具箱的周期性小波变换函数dwtper2.m%
%并使用双正交小波bior3.3进行一层小波变换%
%[LL1,LH1,HL1,HH1]=dwtper2(X,'bior3.3');

Wavelet_Name='db1';
Wavelet_Scale=1;

[LL2,LH2,HL2,HH2]=swt2(double(X(:,:,2)),Wavelet_Scale,Wavelet_Name);
[LL22,LH22,HL22,HH22]=swt2(LL2,1,Wavelet_Name);

%分别针对不同的频带进行向量量化，其中VQ.m置于L3.4节中%
[R_LL22,bitLL22]=VQ(LL22,cbLL22);

%计算图像经过向量量化后的压缩率CR及失真PSNR%
totalbit2=bitLL22   %花费的总位数 57344
%Y=idwtper2(R_LL1,R_LH1,R_HL1,R_HH1,'bior3.3'); %反小波变换
Y22=iswt2(R_LL22,HL22 ,LH22, HH22,'db1');
Y2=iswt2(Y22,HL2 ,LH2, HH2,'db1');

%X(:,:,2)=Y2;


Wavelet_Name='db1';
Wavelet_Scale=1;
[LL3,LH3,HL3,HH3]=swt2(double(X(:,:,3)),Wavelet_Scale,Wavelet_Name);
[LL32,LH32,HL32,HH32]=swt2(LL3,1,Wavelet_Name);

%appcoef2用来提取二维信号小波分解的近似分量

%训练LL1频带码本，将码本大小设为128，维度为4 %
th=0.05;
nc=128;nd=4;
[cbLL32]=LBG(LL32,nc,nd,th); %LBG.m在L3.3节中

X=imread('E:\matlab1\LENAcaise128.bmp','bmp');
%load lena
[M3,N3]=size(X(:,:,3));
%使用Matlab中Wavelet工具箱的周期性小波变换函数dwtper2.m%
%并使用双正交小波bior3.3进行一层小波变换%
%[LL1,LH1,HL1,HH1]=dwtper2(X,'bior3.3');
Wavelet_Name='db1';
Wavelet_Scale=1;
[LL3,LH3,HL3,HH3]=swt2(double(X(:,:,3)),Wavelet_Scale,Wavelet_Name);
[LL32,LH32,HL32,HH32]=swt2(LL3,1,Wavelet_Name);


%分别针对不同的频带进行向量量化，其中VQ.m置于L3.4节中%
[R_LL32,bitLL32]=VQ(LL32,cbLL32);


%计算图像经过向量量化后的压缩率CR及失真PSNR%
totalbit3=bitLL32    %花费的总位数
%Y=idwtper2(R_LL1,R_LH1,R_HL1,R_HH1,'bior3.3'); %反小波变换
Y32=iswt2(R_LL32,HL32 ,LH32, HH32,'db1');
Y3=iswt2(Y32,HL3 ,LH3, HH3,'db1')
%X(:,:,3)=Y3;
RG1=cat(3,Y1,Y2,Y3);

MSE=(sum(sum((double(X(:,:,3))-double(RG1(:,:,3))).^2)))/(M*N)
PSNR=20*log10(255/sqrt(MSE))
CR2=M3*N3*8/totalbit2

figure(200);imshow(uint8(X));title('compressed image');
figure(300);imshow(uint8(RG1));title('compressed555 image');


%[LL1,LH1,HL1,HH1]=swt2(X,Wavelet_Scale,Wavelet_Name);