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Task 4 Run the functions you have defined in Tasks 1 and 2 at least 2 5 times each for different values of N (

Task 4
Run the functions you have defined in Tasks 1 and 2 at least 25 times each for different values of N (2,4,8,16,32) and plot the average run time vs compression ratio (N). Which function runs faster? Why? How does the algorithm scale with compression factor (Which of these: logarithmic, exponential, quadratic, or linear)? In this homework, you will implement a basic compression method for images that will reduce the number of values in color scales
(RGB) to a compression factor.
In MATLAB, images can be represented with 3-dimensional arrays (MxN3) where the first dimension represents height, the second
dimension represents width, and the third dimension represents the color components (RGB). The values in this array can be
between 0-255 with 0 representing the lowest intensity for the color. The compression method you will implement in this homework
will reduce the number of values that the color intensities can take to the compression factor (N) such that instead of using 256 values
to represent the color intensity, the compressed image will use only N distinct intensity values (N should be a power of 2). In
implementation, the original intensity range (0-255) will be divided into N sub-ranges, and the values in each sub-range will be
mapped to a new value. As an example, Table 1 shows the original intensity ranges and mapped values for N=8. Use the following
steps to implement the algorithm for any value of N(N should be a positive number that is a power of 2 and should be less than 256);
Define the lower and upper limits for each sub-range (Hint: For the first sub-range lower limit is 0, and upper limit is (256/N)-1)
Define the new values to which the original values will be mapped (Hint: Pixels having values in the kth subrange will be
mapped to a single value that is {:(k-1)**256N+2562**N)
Map the pixel values in the original image to the new values.
Table 1. Example values for N=8
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