spihttry.m

基于wavelet和bandelet的spiht图像压缩！

clc
clear all
%load woman;
%Orig_I=X(1:256,1:256);
%axis square;


%-----------   Input   ----------------
Orig_I = func_ReadRaw('lena512.raw',512*512,512, 512);
Orig_I=Orig_I(1:256,1:256);
   
 
rate = 0.1;
figure
subplot(121);
 colormap gray(256);
image(Orig_I);
%-----------   Pre-processing   ----------------

OrigSize = size(Orig_I, 1);
max_bits = floor(rate * OrigSize^2);

OutSize = OrigSize;
image_spiht = zeros(size(Orig_I));
% "image " is the input of codec
[nRow, nColumn] = size(Orig_I);


%-----------   Wavelet Decomposition   ----------------
n = size(Orig_I,1);
n_log = log2(n); 
level = n_log;
% wavelet decomposition level can be defined by users manually.

type = 'bior4.4';
[Lo_D,Hi_D,Lo_R,Hi_R] = wfilters(type);

[I_W, S] = func_DWT(Orig_I, level, Lo_D, Hi_D);

%-----------   Coding   ----------------
img_enc = func_SPIHT_Enc(I_W, max_bits, nRow*nColumn, level);   

%-----------   Decoding   ----------------
img_dec = func_SPIHT_Dec(img_enc);

%-----------   Wavelet Reconstruction   ----------------
img_spiht = func_InvDWT(img_dec, S, Lo_R, Hi_R, level);
          
%--------------output image--------------
subplot(122);
  colormap gray(256);
image(img_spiht);

%-----------   PSNR analysis   ----------------
p =psnr(Orig_I,img_spiht);