  The performance of objective image quality assessment (IQA) models has been
evaluated primarily by comparing model predictions to human quality judgments.
Perceptual datasets gathered for this purpose have provided useful benchmarks
for improving IQA methods, but their heavy use creates a risk of overfitting.
Here, we perform a large-scale comparison of IQA models in terms of their use
as objectives for the optimization of image processing algorithms.
Specifically, we use eleven full-reference IQA models to train deep neural
networks for four low-level vision tasks: denoising, deblurring,
super-resolution, and compression. Subjective testing on the optimized images
allows us to rank the competing models in terms of their perceptual
performance, elucidate their relative advantages and disadvantages in these
tasks, and propose a set of desirable properties for incorporation into future
IQA models.
