A part of an image is represented by an n x n matrix. After
performing data compression and then data reconstruction techniques,
the resulting matrix has values that are close to but not exactly equal
to the original matrix. For example, the following 4 x 4 matrix variable
orig_im represents a small part of a true color image, and fin_im
represents the matrix after it has undergone data compression and
then reconstruction.
orig_im =
156 44 129 87
18 158 118 102
80 62 138 78
155 150 241 105
fin_im =
153 43 130 92
16 152 118 102
73 66 143 75
152 155 247 114
Write a script that will simulate this by creating a square matrix of
random integers, each in the range from 0 to 255. It will then modify
this to create the new matrix by randomly adding or subtracting a
random number (in a relatively small range, say 0 to 10) from every
element in the original matrix. Then, calculate the average difference
between the two matrices.
Matlab
Ch13Ex8.m
% Simulates image compression and reconstruction, and
% Calculates average difference between matrices
orig_im = randi([0 255],4,4);
[r, c] = size(orig_im);
offval = randi([-7 9],r,c);
fin_im = orig_im + offval;
avediff = mean(mean(abs(offval)));
fprintf('The average difference is: %f\n',avediff)
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