  Following the major successes of self-attention and Transformers for image
analysis, we investigate the use of such attention mechanisms in the context of
Image Quality Assessment (IQA) and propose a novel full-reference IQA method,
Vision Transformer for Attention Modulated Image Quality (VTAMIQ). Our method
achieves competitive or state-of-the-art performance on the existing IQA
datasets and significantly outperforms previous metrics in cross-database
evaluations. Most patch-wise IQA methods treat each patch independently; this
partially discards global information and limits the ability to model
long-distance interactions. We avoid this problem altogether by employing a
transformer to encode a sequence of patches as a single global representation,
which by design considers interdependencies between patches. We rely on various
attention mechanisms -- first with self-attention within the Transformer, and
second with channel attention within our difference modulation network --
specifically to reveal and enhance the more salient features throughout our
architecture. With large-scale pre-training for both classification and IQA
tasks, VTAMIQ generalizes well to unseen sets of images and distortions,
further demonstrating the strength of transformer-based networks for vision
modelling.
