how to calculate the probability of each element in string

i am having a rgb image i have converted to string as

    rgb_image = imread('lena1.jpg');
     % Display the original image
    figure(1)
    imshow(rgb_image)
    title('Original Image');
    %convert to string
    out=num2str(rgb_image)

now i need to calculate the probability of each pixel value that is now converted to string can anyone help please

[juveria fatima]

rgb_image = imread('lena1.jpg');

 % Display the original image
figure(1)

imshow(rgb_image)

title('Original Image');

%convert to string

out=num2str(rgb_image)

[Jos (10584)]

Why do you need to convert it to a string?

What do you exactly mean by " probability of each element"? Are you perhaps looking for the frequency of each element in the image?

The function imhist might be of help here.

[juveria fatima]

yes you r right!

actually i want to find the probability of each pixel value for huffman code

am having a huffman code working for strings so, i thought of converting the image to string amd club that string to my code

here is my code for strings using huffman

%Target: To huffman encode and decode user entered string %-------------------------------------------------------------------------- string=input('enter the string in inverted commas'); %input string

symbol=[]; %initialise variables

count=[];

j=1;

%------------------------------------------loop to separate symbols and how many times they occur

for i=1:length(string)

flag=0;    

flag=ismember(symbol,string(i));      %symbols

      if sum(flag)==0

      symbol(j) = string(i);

      k=ismember(string,string(i));

      c=sum(k);                         %no of times it occurs  

      count(j) = c;

      j=j+1;

      end

end

ent=0;

total=sum(count); %total no of symbols

prob=[];

%-----------------------------------------for loop to find probability and

%entropy

for i=1:1:size((count)');

prob(i)=count(i)/total;

ent=ent-prob(i)*log2(prob(i));

end

var=0;

%-----------------------------------------function to create dictionary

[dict avglen]=huffmandict(symbol,prob);

% print the dictionary.

         temp = dict;

         for i = 1:length(temp)

         temp{i,2} = num2str(temp{i,2});

         var=var+(length(dict{i,2})-avglen)^2;  %variance calculation

         end

         temp

%-----------------------------------------encoder and decoder functions

sig_encoded=huffmanenco(string,dict)

deco=huffmandeco(sig_encoded,dict);

equal = isequal(string,deco)

%-----------------------------------------decoded string and output %variables

str ='';

for i=1:length(deco)

str= strcat(str,deco(i));

end

str

ent

avglen

var

[Jan]

@juveria fatima: Please format your code properly to make it readable. See http://www.mathworks.com/matlabcentral/answers/13205-tutorial-how-to-format-your-question-with-markup .

Answer 1

[Jan]

Although Huffman encode works on "strings", you can apply it to numbers also, of course. histcounts will be usual for your purpose.

The code can be simplified. E.g.:

flag=ismember(symbol,string(i));      %symbols
if sum(flag)==0

can be written as:

if ~any(string(i) == symbol)

This is at least confusing:

for i=1:1:size((count)');

Note that size replies a vector and 1:size(x) need not be, what you expect. Transposing the array only to get the wanted dimension as first output of size is a waste of time. Better:

for i = 1:size(count, 2)

or because count is a vector:

for i = 1:numel(count)

But this loop is not required at all:

prob = count / total;
ent  = -sum(prob .* log2(prob));

This is very inefficient also:

for i = 1:length(deco)
  str = strcat(str, deco(i));
end

The iterative growing of an array is a very bad programming pattern, because the costs grow exponentially. Better without a loop:

str = char(deco);

Finally: num2str(rgb_image) is a very bad idea. Imagine the first pixel has the red channel value 0.2543. Now you convert this by num2str to '0.2543'. Then the Huffman encoding will consider the probability of digits, but you want to encode the probability of colors. This is something completely different.

So start from scratch. 1. improve the code for the Huffman encoding. 2. modify it such, that it accepts numbers from 0 to 255 as inputs. 3. Convert your RGB image to the uint8 format. Afterwards you can apply the Huffman encoding to your image.

[Nidhi Kumari]

Can you give the full code which is actually working properly?

[Jan]

@Nidhi Kumari: The original code was strangly formatted and it would cost some time, to guess, what is exactly meant. If I spend the time to do this, it is not sure, if the code matchs your needs exactly. So better post a new question and try to solve your problem there.