sampletest.m

EZW法是一個用matlab寫的程式,是植基於小波的影像壓縮轉換

% This is a test program that tests the EZW encoder and decoder
% matrix X is an example matrix same as Shapiro's paper given


% Written by xiangang li
%
% This function calls:
%    dominantpass.m
%    subordinatepass.m
%    checkdescents1.m
%    checkchildren.m
%    mapping.m



X=[63 -34 49 10 7 13 -12 7;
   -31 23 14 -13 3 4 6 -1;
   15 14 3 -12 5 -7 3 9;
   -9 -7 -14 8 4 -2 3 2;
   -5 9 -1 47 4 6 -2 2;
   3 0 -3 2 3 -2 0 4;
   2 -3 6 -4 3 6 3 6;
   5 11 5 6 0 3 -4 4];

X0=X;

Y0=max(X);
     Y1=max(Y0); 
     for i=0:20;
        if 2^i<=Y1 & 2^i>0.5*Y1;
           threshold=2^i;   % get initial threshold T0;
           initialthreshold=threshold; % get initial threshold T0;
           laststeplevel=i+1;% last step level
           break;
        end;
     end;
     
     
     sublist=[];
     sub_list=[];
     [xx,yy]=size(X);
     
  
A=mapping(xx);
[m,n]=size(A);
global N;      % Let Morton scanorder vector as a global variable
 N=zeros(m*n,2);

     for i=1:m,
        for j=1:n,
            
                 N(A(i,j),1)=i;
                 N(A(i,j),2)=j;
                
         end
     end

       order=1;  
while threshold ~= 0.5,  % if threshold~=1, do dominantpass and subordinatepass.
         
   threshold
   
   %Dominant Pass
   
   [D,X,sublist,sub_list] = dominantpass(X,threshold,sublist,sub_list); 
   
   DD{order}=D
   significantlist{order}=sub_list;
     
    %Subordinate pass
    
    threshold=threshold/2;
    if threshold ==0.5,
        break;
    end
    
    
   S = subordinatepass(sublist,threshold);
   SS{order}=S
   order=order+1;
    
end

  
%*********************************%   
%*********EZW decoder*************%
%*********************************%

 global N;
 [m,n]=size(N);% the size of initial image
               % m is the pixels of initial image
 XX=zeros(sqrt(m)); % initialize the reconstructured image to zero;
 threshold=initialthreshold; % initial theshold ;
 
 sublist=[]; % sublist is the new position matrix 
             % for all significant coefficients 'p' and 'n';
             
             
for level=1:laststeplevel, 
                
            RR=zeros(size(XX)); % reference matrix RR;
  
     [a,b]=size(DD{level}); % ?    
 
 % dominant pass
 
     
        i=1; j=1;
     while j<=b,
     
         if RR(N(i,1),N(i,2))==0
                      
              if DD{level}(j)=='p'
                  if threshold==1
                     XX(N(i,1),N(i,2))=threshold; 
                 else
                     XX(N(i,1),N(i,2))=1.5*threshold;
                 end
                
              end
     
              if DD{level}(j)=='n'
                 if threshold==1
                     XX(N(i,1),N(i,2))=-threshold; 
                 else
                     XX(N(i,1),N(i,2))=-1.5*threshold;
                 end
              end
         
                
              if DD{level}(j)=='t'& A(N(i,1),N(i,2))<=m/4
                 RR=checkchildren(i,RR);% all zerotree's descendants are set to 1.
              end
             
              RR(N(i,1),N(i,2))=1; %reference matrix =1;
              i=i+1;
              j=j+1;
               
        else i=i+1;
               
             
        end
         
    end
 
 
 % subordinate pass
 
 
 [xx,yy]=size(significantlist{level});
 threshold=threshold/2;
 
   for i=1:xx,
       
       if level==laststeplevel|threshold==0.5
           break
       end
       
      if SS{level}(i)==1
         if XX(sub_list(i,1),sub_list(i,2))>0;
           
            XX(sub_list(i,1),sub_list(i,2))= fix(XX(sub_list(i,1),sub_list(i,2))+ threshold/2);
            
         else 
             
            XX(sub_list(i,1),sub_list(i,2))= fix(XX(sub_list(i,1),sub_list(i,2))-threshold/2);
           
         end
      end
      
      if SS{level}(i)==0
         if XX(sub_list(i,1),sub_list(i,2))>0;
           
            XX(sub_list(i,1),sub_list(i,2))= fix(XX(sub_list(i,1),sub_list(i,2))-threshold/2);
            
         else 
             
            XX(sub_list(i,1),sub_list(i,2))= fix(XX(sub_list(i,1),sub_list(i,2))+threshold/2);
         
         end
      end
   end
   
    

      threshold
      level
      XX
      
end
initialiamge=X0
reconstructedimage=XX
difference=XX-X0