  Difference image analysis (DIA) is an effective technique for obtaining
photometry in crowded fields, relative to a chosen reference image. As yet,
however, optimal reference image selection is an unsolved problem. We examine
how this selection depends on the combination of seeing, background and
detector pixel size. Our tests use a combination of simulated data and quality
indicators from DIA of well-sampled optical data and under-sampled
near-infrared data from the OGLE and VVV surveys, respectively. We search for a
figure-of-merit (FoM) which could be used to select reference images for each
survey. While we do not find a universally applicable FoM, survey-specific
measures indicate that the effect of spatial under-sampling may require a
change in strategy from the standard DIA approach, even though seeing remains
the primary criterion. We find that background is not an important criterion
for reference selection, at least for the dynamic range in the images we test.
For our analysis of VVV data in particular, we find that spatial under-sampling
is best handled by reversing the standard DIA procedure and convolving target
images to a better-sampled (poor seeing) reference image.
