  Subjective image quality databases are a major source of raw data on how the
visual system works in naturalistic environments. These databases describe the
sensitivity of many observers to a wide range of distortions (of different
nature and with different suprathreshold intensities) seen on top of a variety
of natural images. They seem like a dream for the vision scientist to check the
models in realistic scenarios.
  However, while these natural databases are great benchmarks for models
developed in some other way (e.g. by using the well-controlled artificial
stimuli of traditional psychophysics), they should be carefully used when
trying to fit vision models. Given the high dimensionality of the image space,
it is very likely that some basic phenomenon (e.g. sensitivity to distortions
in certain environments) are under-represented in the database. Therefore, a
model fitted on these large-scale natural databases will not reproduce these
under-represented basic phenomena that could otherwise be easily illustrated
with well selected artificial stimuli.
  In this work we study a specific example of the above statement. A
wavelet+divisive normalization layer of a sensible cascade of linear+nonlinear
layers fitted to maximize the correlation with subjective opinion of observers
on a large image quality database fails to reproduce basic crossmasking. Here
we outline a solution for this problem using artificial stimuli. Then, we show
that the resulting model is also a competitive solution for the large-scale
database. In line with Rust and Movshon (2005), our report (misrepresentation
of basic visual phenomena in subjectively-rated natural image databases) is an
additional argument in praise of artifice.
