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

VTAMIQ: Transformers for Attention Modulated Image Quality Assessment