So a 1D mean filter of size two with a sliding window defined as W below could be considered as having a stride of one.
Wn=(Xn + Xn+1) / 2
where as if I were to define a 1D 'stepped mean filter' with a stride of two and labeled U below.
Un= (X2n + X2n+1) / 2
where the number of samples in U (and also V) would be half that of W.
My questions are the following
- Is U still a filter (I'm guessing it is even though not fitting with the traditional kernel convolution implementation I associate with filters)
- What is the technical language one could use to communicate and research questions regarding filter/non-filter such as just given?
- All though not directly related but since I have the floor is, if V and U would produce equivalent results (my intuition says no)?