  We present a full reference, perceptual image metric based on VGG-16, an
artificial neural network trained on object classification. We fit the metric
to a new database based on 140k unique images annotated with ground truth by
human raters who received minimal instruction. The resulting metric shows
competitive performance on TID 2013, a database widely used to assess image
quality assessments methods. More interestingly, it shows strong responses to
objects potentially carrying semantic relevance such as faces and text, which
we demonstrate using a visualization technique and ablation experiments. In
effect, the metric appears to model a higher influence of semantic context on
judgments, which we observe particularly in untrained raters. As the vast
majority of users of image processing systems are unfamiliar with Image Quality
Assessment (IQA) tasks, these findings may have significant impact on
real-world applications of perceptual metrics.
