pp.m

将PSO和LBG结合在一步迭代过程中

% =====================================================================
%                 PP algorithm 
%     Start Date: 15/07/2006
%   Last Changed: 21/08/2006
%          Usage: Before runing the program, make sure experimental image exist 
%  
%  Specification: The algorithm, proposed by Ji and Liao in 2006, incorporating
%                 both PSO algorithm and LBG to one iteration and using  
%                 particle-pair to explore in the problem space
%
% Copyright (c) Ji and Liao in 2006
% All rights Reserved
%==========================================================================
warning off MATLAB:divideByZero
clear all;
psnr1=0;
k=2;
particle=[];
pbest_value=[];
t=[];
gbest_value_smallpop=[];
for i=1:k
    [particlenext,pbestnext,t1,gbest_value]=PSOLBG1;
    particle=[particle;particlenext];
    pbest_value=[pbest_value;pbestnext];
    gbest_value_smallpop=[gbest_value_smallpop;gbest_value];
    t=[t,t1];
end
[gbest,psnr1,t2,gbest_value_pop3,tempz_cluster]=PSOLBG2(particle,pbest_value);
t=[t,t2];

timeconsumed=sum(t)/60 %time used in minute
psnr1  %  the psnr value of last iteration

% evaluate the distortion below
GenTrainingVector;
num_cluster=256;
num_trainingvector=4096;
for j=1:num_trainingvector 
    [C(j),I(j)]=min(sum((repmat(trainingvector(j,:),num_cluster,1)-tempz_cluster).^2,2));
end
Distortion=sum(C)/4096