Image compression huffman coding
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%clearing all variableas and screen
clear all;
close all;
clc;
%Reading image
a=imread('jpeg-image-compression-1-638.JPG');
figure,imshow(a)
%converting an image to grayscale
I=rgb2gray(a);
%size of the image
[m,n]=size(I);
Totalcount=m*n;
%variables using to find the probability
cnt=1;
sigma=0;
%computing the cumulative probability.
for i=0:255
k=I==i;
count(cnt)=sum(k(:))
%pro array is having the probabilities
pro(cnt)=count(cnt)/Totalcount;
sigma=sigma+pro(cnt);
cumpro(cnt)=sigma;
cnt=cnt+1;
end;
%Symbols for an image
symbols = [0:255];
%Huffman code Dictionary
dict = huffmandict(symbols,pro);
%function which converts array to vector
vec_size = 1;
for p = 1:m
for q = 1:n
newvec(vec_size) = I(p,q);
vec_size = vec_size+1;
end
end
%Huffman Encodig
hcode = huffmanenco(newvec,dict);
%Huffman Decoding
dhsig1 = huffmandeco(hcode,dict);
%convertign dhsig1 double to dhsig uint8
dhsig = uint8(dhsig1);
%vector to array conversion
dec_row=sqrt(length(dhsig));
dec_col=dec_row;
%variables using to convert vector 2 array
arr_row = 1;
arr_col = 1;
vec_si = 1;
for x = 1:m
for y = 1:n
back(x,y)=dhsig(vec_si);
arr_col = arr_col+1;
vec_si = vec_si + 1;
end
arr_row = arr_row+1;
end
%converting image from grayscale to rgb
[deco, map] = gray2ind(back,256);
RGB = ind2rgb(deco,map);
imwrite(RGB,'decoded.JPG');
%end of the huffman coding
This code works fine but the image decoded is grey in colour. I cannot find anything wrong in it please help
5 Comments
maha asiri
on 22 Nov 2021
Moved: Walter Roberson
on 10 Aug 2024 at 18:01
did you find the anwer for this code
couse i want it
Answers (3)
Saherish Pathan
on 10 Jan 2022
Edited: Walter Roberson
on 10 Aug 2024 at 18:00
clear all;
close all;
clc;
%Reading image
a=imread('jpeg-image-compression-1-638.JPG');
figure,imshow(a)
%converting an image to grayscale
I=rgb2gray(a);
%size of the image
[m,n]=size(I);
Totalcount=m*n;
%variables using to find the probability
cnt=1;
sigma=0;
%computing the cumulative probability.
for i=0:255
k=I==i;
count(cnt)=sum(k(:))
%pro array is having the probabilities
pro(cnt)=count(cnt)/Totalcount;
sigma=sigma+pro(cnt);
cumpro(cnt)=sigma;
cnt=cnt+1;
end;
%Symbols for an image
symbols = [0:255];
%Huffman code Dictionary
dict = huffmandict(symbols,pro);
%function which converts array to vector
vec_size = 1;
for p = 1:m
for q = 1:n
newvec(vec_size) = I(p,q);
vec_size = vec_size+1;
end
end
%Huffman Encodig
hcode = huffmanenco(newvec,dict);
%Huffman Decoding
dhsig1 = huffmandeco(hcode,dict);
%convertign dhsig1 double to dhsig uint8
dhsig = uint8(dhsig1);
%vector to array conversion
dec_row=sqrt(length(dhsig));
dec_col=dec_row;
%variables using to convert vector 2 array
arr_row = 1;
arr_col = 1;
vec_si = 1;
for x = 1:m
for y = 1:n
back(x,y)=dhsig(vec_si);
arr_col = arr_col+1;
vec_si = vec_si + 1;
end
arr_row = arr_row+1;
end
%converting image from grayscale to rgb
[deco, map] = gray2ind(back,256);
RGB = ind2rgb(deco,map);
imwrite(RGB,'decoded.JPG');
0 Comments
Abdel Rahman Bekawi
on 11 Jan 2020
Edited: Abdel Rahman Bekawi
on 12 Jan 2020
well done, your code is great!
however, the thing that makes this logical error is that your map channels are equal to each other, hence you will get grayed scale image as well, so to overcome this issue, I recommend you work with indexed images.
% try the following
[indexed, colormap] = rgb2ind(rgbimage, number_of_colors);
% do all the processing on this indexed format and then at the end
RGB = ind2rgb(back, colormap);
% by this you should get a compressed colored image
I hope this helps!
3 Comments
Walter Roberson
on 31 Aug 2020
Yes, people have done that. Just reshape the image into a vector, and Huffman encode that. If you save or transmit the encoded image, remember to store the dictionary and the image size so that you can reconstruct afterwards.
Idin Motedayen-Aval
on 3 Jun 2024
Edited: Idin Motedayen-Aval
on 3 Jun 2024
This page is still getting a fair number of views, so I wanted to summarize the discussion.
- As it has been pointed out, the problem in the original code is the line "I = rgb2gray(a);". This line converts the image to grayscale, so everything is grayscale from that point on. The color information is lost and cannot be recovered.
- The right approach is to use indexed images as pointed out by @abdel rahman.
- If you want to store/transmit the compressed image, as @Walter Roberson pointed out a few times, "you should also be storing the rgb table and the Huffman dictionary."
- The OP's code is a mere academic exercise in Huffman coding an image, and then decoding it to recover the original image (since Huffman coding is lossless, the recovered image will be indentical to the original).
- I cleaned up the code a bit to show how it might run in MATLAB (it can be cleaned up much more; I preserved much of the original code for traceability)
%clearing all variableas and screen
clear all;
close all;
clc;
number_of_colors = 256;
%Reading image
a=imread('peppers.png');
figure(1),imshow(a)
%converting an image to grayscale
% I=rgb2gray(a);
% Use indexed image instead of grayscale
[I, myCmap] = rgb2ind(a, number_of_colors);
%size of the image
[m,n]=size(I);
Totalcount=m*n;
%variables using to find the probability
cnt=1;
% sigma=0;
%computing the cumulative probability.
pro = zeros(256,1);
for i=0:255
k=(I==i);
count=sum(k(:));
%pro array is having the probabilities
pro(cnt)=count/Totalcount;
% sigma=sigma+pro(cnt); <-- Not needed here
% cumpro(cnt)=sigma; <-- Not needed here
cnt=cnt+1;
end
% Probablities can also be found using histcounts
pro1 = histcounts(I,0:256,'Normalization','probability');
cumpro = cumsum(pro); % if the cumulative sum is needed
sigma = sum(pro); % if the sum is needed; should always be 1.0
%Symbols for an image
symbols = 0:255;
%Huffman code Dictionary
dict = huffmandict(symbols,pro);
%function which converts array to vector
newvec = reshape(I,[numel(I),1]);
%Huffman Encodig
hcode = huffmanenco(newvec,dict);
%Huffman Decoding
dhsig1 = huffmandeco(hcode,dict);
%convertign dhsig1 double to dhsig uint8
dhsig = uint8(dhsig1);
%vector to array conversion
back = reshape(dhsig,[m n]);
%converting image from grayscale to rgb
% [deco, map] = gray2ind(back,256);
% RGB = ind2rgb(deco,map);
RGB = ind2rgb(back,myCmap);
imwrite(RGB,'decoded.JPG');
figure(2),imshow(RGB)
%end of the huffman coding
1 Comment
Walter Roberson
on 10 Aug 2024 at 18:05
[I, myCmap] = rgb2ind(a, number_of_colors);
Note that with this approach of converting to indexed image, the restored image will not generally be identical to the original image. To have the restored image be identical to the original image, reshape the image from being m x n x 3 to being m x (n*3) and then encode that effectively-grayscale result.
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