  Assessing the quality of aperture synthesis maps is relevant for benchmarking
image reconstruction algorithms, for the scientific exploitation of data from
optical long-baseline interferometers, and for the design/upgrade of
new/existing interferometric imaging facilities. Although metrics have been
proposed in these contexts, no systematic study has been conducted on the
selection of a robust metric for quality assessment. This article addresses the
question: what is the best metric to assess the quality of a reconstructed
image? It starts by considering several metrics, and selecting a few based on
general properties. Then, a variety of image reconstruction cases is
considered. The observational scenarios are phase closure and phase referencing
at the Very Large Telescope Interferometer (VLTI), for a combination of two,
three, four and six telescopes. End-to-end image reconstruction is accomplished
with the MiRA software, and several merit functions are put to test. It is
found that convolution by an effective point spread function is required for
proper image quality assessment. The effective angular resolution of the images
is superior to naive expectation based on the maximum frequency sampled by the
array. This is due to the prior information used in the aperture synthesis
algorithm and to the nature of the objects considered. The l1 norm is the most
robust of all considered metrics, because being linear it is less sensitive to
image smoothing by high regularisation levels. For the cases considered, this
metric allows the implementation of automatic quality assessment of
reconstructed images, with a performance similar to human selection.
