Image Compression using RLC_imagecompression_源码

%% Matlab code for Image Compression Using Run Length Encoding clc; clear; close all; %% Set Quantization Parameter quantizedvalue=100; %% Read Input Image InputImage=imread('cameraman.tif'); [row col p]=size(InputImage); %% Wavelet Decomposition [LL LH HL HH]=dwt2(InputImage,'haar'); WaveletDecomposeImage=[LL,LH;HL,HH]; imshow(WaveletDecomposeImage,[]); %% Uniform Quantzation QuantizedImage= WaveletDecomposeImage/quantizedvalue; QuantizedImage= round(QuantizedImage); ImageArray=toZigzag(QuantizedImage) %% Run Length Encoding j=1; a=length(ImageArray); count=0; for n=1:a b=ImageArray(n); if n==a count=count+1; c(j)=count; s(j)=ImageArray(n); elseif ImageArray(n)==ImageArray(n+1) count=count+1; elseif ImageArray(n)==b count=count+1; c(j)=count; s(j)=ImageArray(n); j=j+1; count=0; end end %% Transmission Variables c contains count s contains symbol c; s; %% Calculation Bit Cost InputBitcost=row*col*8; InputBitcost=(InputBitcost); c1=length(c); s1=length(s); OutputBitcost= (c1*8)+(s1*8); OutputBitcost=(OutputBitcost); %% Run Length Decoding g=length(s); j=1; l=1; for i=1:g v(l)=s(j); if c(j)~=0 w=l+c(j)-1; for p=l:w v(l)=s(j); l=l+1; end end j=j+1; end ReconstructedImageArray=v; %% Inverse ZigZag ReconstructedImage=invZigzag(ReconstructedImageArray) %% Inverse Quantization ReconstructedImage=ReconstructedImage*quantizedvalue; %% Wavelet Reconstruction sX = size(ReconstructedImage); cA1 = ReconstructedImage(1:(sX(1)/2), 1:(sX(1)/2)); cH1 = ReconstructedImage(1:(sX(1)/2), (sX(1)/2 + 1):sX(1)); cV1 = ReconstructedImage((sX(1)/2 + 1):sX(1), 1:(sX(1)/2)); cD1 = ReconstructedImage((sX(1)/2 + 1):sX(1), (sX(1)/2 + 1):sX(1)); ReconstructedImage = idwt2(cA1,cH1,cV1,cD1,'haar'); %% Visualize both Input and Reconstructed Images subplot(1,2,1);imshow(InputImage);title('Input Image'); subplot(1,2,2);imshow(ReconstructedImage,[]);title('Reconstructed Image'); %% Calculating PSNR and MSE InputImage=double(InputImage); ReconstructedImage=double(ReconstructedImage); n=size(InputImage); M=n(1); N=n(2); MSE = sum(sum((InputImage-ReconstructedImage).^2))/(M*N); PSNR = 10*log10(256*256/MSE); disp('MSE'); disp(MSE); disp('PSNR'); disp(PSNR); disp('InputBitcost'); disp(InputBitcost); disp('OutputBitcost'); disp(OutputBitcost);