CV2 resizing with CNN

Question

I am using CV2 to resize various images with different dimensions(i.e. 70*300, 800*500, 60*50) to a specific (200*200) pixels dimension. Later, I am feeding the pictures to CNN algorithm to classify the images. (my understanding that pictures must have the same size when fed into CNN).

My questions:

1- How low picture resolutions are converted into higher one and how higher resolutions are converted into lower one? Will this affect the stored information in the pictures

2- Is it good practice to use this approach with CNN? Or is it better to Pad zeros to the end of the image to get the desired resolution? I have seen many researchers pad the end of a file with zeros when trying to detect Malware files to have a common dimension for all the files. Does this mean that padding is more accurate than resizing?

Answer 1

1. Using interpolation. https://chadrick-kwag.net/cv2-resize-interpolation-methods/

Definitely, resizing is a lossy process and you'll lose information.

2. Both are okay and used depending on the needs. Resizing is also equally applicable. If your CNN can't differentiate between the original and resized images it must be a badly overfitted one. Resizing is a very light regularization too, even it's advisable to apply more augmentation schemes on the images before CNN training.