K-mean assignment doubt

Course Q&A Machine Learning Specialization Unsupervised Learning, Recommenders, Reinforcement
1

After finding the top 𝐾=16�=16 colors to represent the image, you can now assign each pixel position to its closest centroid using the find_closest_centroids function.

  • This allows you to represent the original image using the centroid assignments of each pixel.
  • Notice that you have significantly reduced the number of bits that are required to describe the image.
    • The original image required 24 bits (i.e. 8 bits x 3 channels in RGB encoding) for each one of the 128×128128×128 pixel locations, resulting in total size of 128×128×24=393,216128×128×24=393,216 bits.
    • The new representation requires some overhead storage in form of a dictionary of 16 colors, each of which require 24 bits, but the image itself then only requires 4 bits per pixel location.
    • The final number of bits used is therefore 16×24+128×128×4=65,92016×24+128×128×4=65,920 bits, which corresponds to compressing the original image by about a factor of 6.

Can someone explain me the last line calculation of total bits after compression

2

To recap:
The original image:

  • 24 bits per pixel
  • the image is 128 x 128 pixels.
  • So the total amount of storage is 24 * 128 * 128 = 393,216 bits.

The compressed image:

  • the color map has 16 centroids of 24 bits each, for 384 bits.
  • the image is 128 x 128 pixels.
  • each pixel is encoded as a 4-bit value (the index into the color map)
  • The amount of storage for the image data is (4 * 128 * 128) = 65,536 bits
  • The total amount of storage to represent the image is 65,536 + 384 = 65,920 bits.

That’s about a 6:1 compression.

3

If you post a specific link to the information you quoted, I’ll check whether there are some typos.