  The observations of solar photosphere from the ground encounter significant
problems due to the presence of Earth's turbulent atmosphere. Prior to applying
image reconstruction techniques, the frames obtained in most favorable
atmospheric conditions (so-called lucky frames) have to be carefully selected.
However, the estimation of the quality of images containing complex
photospheric structures is not a trivial task and the standard routines applied
in night-time Lucky Imaging observations are not applicable. In this paper we
evaluate 36 methods dedicated for the assessment of image quality which were
presented in the rich literature over last 40 years. We compare their
effectiveness on simulated solar observations of both active regions and
granulation patches, using reference data obtained by the Solar Optical
Telescope on the Hindoe satellite. To create the images affected by a known
degree of atmospheric degradation, we employ the Random Wave Vector method
which faithfully models all the seeing characteristics. The results provide
useful information about the methods performance depending on the average
seeing conditions expressed by the ratio of the telescope's aperture to the
Fried parameter, $D/r_0$. The comparison identifies three methods for
consideration by observers: Helmli and Scherer's Mean, Median Filter Gradient
Similarity, and Discrete Cosine Transform Energy Ratio. While the first one
requires less computational effort and can be used effectively virtually in any
atmospherics conditions, the second one shows its superiority at good seeing
($D/r_0<4$). The last one should be considered mainly for the post-processing
of strongly blurred images.
