X=imread('wed2.jpg');
r=X(:,:,1);g=X(:,:,2);b=X(:,:,3);
Xgray=0.2126*r + 0.7152*g +0.0722*b;
original=figure;
set(original, 'name', 'Original BW','numbertitle','off')
imagesc(Xgray);colormap(gray)
axis square;

for i=1:75
    for j=1:75
        x=Xgray((i-1)*8+1:(i-1)*8+8,(j-1)*8+1:(j-1)*8+8); %extract 8x8 pixel block

        %Start of Compression Process
        xd=double(x); %convert uint8 to double
        xc=xd-128; %center matrix over zero
        Y=dct(dct(xc')'); %Apply the 2D-DCT, then Y is the transform matrix

        p=4;    %p is the loss parameter
        Q=p*8./hilb(8); %use hilbert matrix to define the linear quantization matrix
        Yq=round(Y./Q); %replace Y by the compressed matrix (Quantize)

        %Start of Decompression Process
        Ydq=Yq.*Q; %dequantization
        Xdq=idct2(Ydq); %inverse DCT transform
        Xe=Xdq+128; %un-center
        Xf=uint8(Xe); %convert back to uint8
        Xout((i-1)*8+1:(i-1)*8+8,(j-1)*8+1:(j-1)*8+8)=Xf;

    end
end

figure('name', 'After, p=4','numbertitle','off');
imagesc(Xout);colormap(gray) %display the 8x8 block after compression

Published with MATLAB® R2013a