  In this paper, we address the well-known image quality assessment problem but
in contrast from existing approaches that predict image quality independently
for every images, we propose to jointly model different images depicting the
same content to improve the precision of quality estimation. This proposal is
motivated by the idea that multiple distorted images can provide information to
disambiguate image features related to content and quality. To this aim, we
combine the feature representations from the different images to estimate a
pseudo-reference that we use to enhance score prediction. Our experiments show
that at test-time, our method successfully combines the features from multiple
images depicting the same new content, improving estimation quality.
