Use Principal Component Analysis to compress the same images as part one above. As before, you are free to use any language, library, and package. Just be sure to specify it in the README file.
Some resources for learning about PCA are below:
- http://people.ciirc.cvut.cz/~hlavac/TeachPresEn/11ImageProc/15PCA.pdf
- http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-45082012000200004
- https://www.kaggle.com/marnixk/image-compression-with-pca-from-scratch-math
Input: You will use the same 3 images that you used in part 1 of this section.
Parameters: It is up to you to select the best value of the number of clusters and any other parameters for the algorithm. You should try at least 3 different values of the number of principal components for each image.
Output: You should include the output of the 3 images i.e. compressed images for any one value of number of principal components. Create a folder called "compressedImages" and place the images there.
How to run: Please include instructions on how to run your code in the report file. Do not use hard-coded paths or parameters.