  The latest advances in full-reference image quality assessment (IQA) involve
unifying structure and texture similarity based on deep representations. The
resulting Deep Image Structure and Texture Similarity (DISTS) metric, however,
makes rather global quality measurements, ignoring the fact that natural
photographic images are locally structured and textured across space and scale.
In this paper, we describe a locally adaptive structure and texture similarity
index for full-reference IQA, which we term A-DISTS. Specifically, we rely on a
single statistical feature, namely the dispersion index, to localize texture
regions at different scales. The estimated probability (of one patch being
texture) is in turn used to adaptively pool local structure and texture
measurements. The resulting A-DISTS is adapted to local image content, and is
free of expensive human perceptual scores for supervised training. We
demonstrate the advantages of A-DISTS in terms of correlation with human data
on ten IQA databases and optimization of single image super-resolution methods.
