  We propose a new class of kernels to simplify the design of filters for image
interpolation and resizing. Their properties are defined according to two
parameters, specifying the width of the transition band and the height of a
unique sidelobe. By varying these parameters it is possible to efficiently
explore the space with only the filters that are suitable for image
interpolation and resizing, and identify the filter that is best for a given
application. These two parameters are also sufficient to obtain very good
approximations of many commonly-used interpolation kernels. We also show that,
because the Fourier transforms of these kernels have very fast decay, these
filters produce better results when time-stretched for image downsizing.
