  Predicting the quality of multimedia content is often needed in different
fields. In some applications, quality metrics are crucial with a high impact,
and can affect decision making such as diagnosis from medical multimedia. In
this paper, we focus on such applications by proposing an efficient and shallow
model for predicting the quality of medical images without reference from a
small amount of annotated data. Our model is based on convolution
self-attention that aims to model complex representation from relevant local
characteristics of images, which itself slide over the image to interpolate the
global quality score. We also apply domain adaptation learning in unsupervised
and semi-supervised manner. The proposed model is evaluated through a dataset
composed of several images and their corresponding subjective scores. The
obtained results showed the efficiency of the proposed method, but also, the
relevance of the applying domain adaptation to generalize over different
multimedia domains regarding the downstream task of perceptual quality
prediction. \footnote{Funded by the TIC-ART project, Regional fund (Region
Centre-Val de Loire)}
