That's true in the 1D case as well. That requires upsampling with information generation before downsampling. Using priori to guess missing information is a

That's true in the 1D case as well. That requires upsampling with information generation before downsampling. Using priori to guess missing information is a task that will never be finished and is interesting. It isn't necessary for a satisfactory downsampling result.