Howtosignalprocessing
- Can we apply PCA on images?
- How does PCA work on image compression?
- Where should you not use PCA?
- How do I split an image into channels using PCA?
Can we apply PCA on images?
One of the use cases of PCA is that it can be used for image compression — a technique that minimizes the size in bytes of an image while keeping as much of the quality of the image as possible.
How does PCA work on image compression?
How does PCA work on Image Compression? The image is a combination of pixels in rows placed one after another to form one single image each pixel value represents the intensity value of the image, so if you have multiple images we can form a matrix considering a row of pixels as a vector.
Where should you not use PCA?
PCA should be used mainly for variables which are strongly correlated. If the relationship is weak between variables, PCA does not work well to reduce data. Refer to the correlation matrix to determine. In general, if most of the correlation coefficients are smaller than 0.3, PCA will not help.
How do I split an image into channels using PCA?
Splitting the Image in R,G,B Arrays
blue,green,red = cv2. split(img) #it will split the original image into Blue, Green and Red arrays. An important point here to note is, OpenCV will split into Blue, Green, and Red channels instead of Red, Blue, and Green. Be very careful of the sequence here.
