  Super-resolution results are usually measured by full-reference image quality
metrics or human rating scores. However, these evaluation methods are general
image quality measurement, and do not account for the nature of the
super-resolution problem. In this work, we analyze the evaluation problem based
on the one-to-many mapping nature of super-resolution, and propose a novel
distribution-based metric for super-resolution. Starting from the distribution
distance, we derive the proposed metric to make it accessible and easy to
compute. Through a human subject study on super-resolution, we show that the
proposed metric is highly correlated with the human perceptual quality, and
better than most existing metrics. Moreover, the proposed metric has a higher
correlation with the fidelity measure compared to the perception-based metrics.
To understand the properties of the proposed metric, we conduct extensive
evaluation in terms of its design choices, and show that the metric is robust
to its design choices. Finally, we show that the metric can be used to train
super-resolution networks for better perceptual quality.
