Image compression huffman coding

97 次查看（过去 30 天）
lavanya vs
lavanya vs 2019-4-29
评论： Walter Roberson 2024-8-10
%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 个评论
显示 3更早的评论
maha asiri
maha asiri 2021-11-22
移动：Walter Roberson 2024-8-10
did you find the anwer for this code
couse i want it

请先登录，再进行评论。

请先登录，再回答此问题。

回答（3 个）

Saherish Pathan
Saherish Pathan 2022-1-10
编辑：Walter Roberson 2024-8-10
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');
Abdel Rahman Bekawi
编辑：Abdel Rahman Bekawi 2020-1-12
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 个评论
显示 1更早的评论
hamza munawar
hamza munawar 2020-8-31
have anyone done the huffman encoding on a 2D image in matlab???
Walter Roberson
Walter Roberson 2020-8-31
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.

请先登录，再进行评论。

Idin Motedayen-Aval
编辑：Idin Motedayen-Aval 2024-6-3
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 个评论
Walter Roberson
Walter Roberson 2024-8-10
[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.

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Denoising and Compression 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by