Update README.md

Author: tenosel

README.md

@@ -69,8 +69,11 @@ In this task the goal is to reduce the storage requirement of an image with widt
 1. Open the file `src/ex2_image_compression.py`. The image is loaded in the `main` function using the `load_image` function. Inspect the `input_img` variable and print the information about its dimensions.
 
 Implement the `compress_colorspace` function using the k-means algorithm: 
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 2. Reshape the input image into $(w\cdot h, 3)$ to perform clustering on colors.
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 3. Use `MiniBatchKMeans` to cluster the image into $k$ clusters.
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 4. Return a compressed image where the number of unique colors where reduced from $256^3$ to $k$ via k-means clustering. The compressed image must have the same shape as the original one.
 
 5. Use `compress_colorspace` in your `main` function to compress the image for $k \in \{2,8,64,256\}$ and plot the result using imshow. Set the corresponding value of $k$ as title for each result.