codebook8_512.m

基于自组织神经网络的矢量量化编码Ｍatlab程序

%function design()
clear all;
figure(1);
sig=imread('lena1.bmp');
sig=double(sig)/255;
[m_sig,n_sig]=size(sig);
siz_word=8;
siz_book=512;
imshow(sig);
num=m_sig/siz_word;
ss=siz_word*siz_word;
nn=num*num;
N=512;
k=64;
re_sig=[];
for i=1:m_sig
    for j=1:n_sig
        f1=floor(i./siz_word);
        m1=mod(i,siz_word);
        if m1==0
            m1=siz_word;
            f1=f1-1;
        end
        f2=floor(j./siz_word);
        m2=mod(j,siz_word);
        if m2==0
            m2=siz_word;
            f2=f2-1;
        end
        re_sig(num*f1+f2+1,siz_word*(m1-1)+m2)=sig(i,j);
    end
end
codebook=[];
for i=1:siz_book
    r=floor(rand*nn)+1;
    codebook=[codebook;re_sig(r,:)];
end

d1=0.0;
yjl=codebook;
tic
for i=1:siz_book
    VectorNumber(i)=0;
end
for i=1:nn
   CodeNumber(i)=1;
   min=VectorDistance(ss,re_sig(i,:),codebook(1,:));
   
       for j=2:siz_book
           d0=0.0;
           for l=1:ss
               d0=d0+(re_sig(i,l)-codebook(j,l)).^2;
               if d0>=min
                  break;
               end
           end
           if d0<min
              min=d0;
              CodeNumber(i)=j;
           end
       end
       VectorNumber(CodeNumber(i))=VectorNumber(CodeNumber(i))+1;
       
       T=20;
       yj=codebook(CodeNumber(i),1:k);
       for t=1:T
           alpha=0.5*exp(-t/T);
           yj=yj+alpha*(re_sig(i,1:k)-yj);
       end
       yjl(CodeNumber(i),1:k)=yj;
                        
   d1=d1+min;
end
toc
t=toc
codebook=yjl;

save codebook8_512 codebook;