I don’t really understand the question. Transforms are applied over original data usually to reduce the dimensionality.
It’s not the same to have a workflow of full HD images which are 1080 p than having 120p images. The amount of data, working time etcetera you need is totally different.
Transforms are also used to normalize data or to create data augmentation (by fliping images for example).
But the most important part is that these transformations are non-differentiable operations, therefore they can never be part of a NN since you wouldn’t be able to backpropagate error through them.
In fact, activation functions are “kind of” non linear transformations but they are differentiable