  Image based Deep Feature Quality Metrics (DFQMs) have been shown to better
correlate with subjective perceptual scores over traditional metrics. The
fundamental focus of these DFQMs is to exploit internal representations from a
large scale classification network as the metric feature space. Previously, no
attention has been given to the problem of identifying which layers are most
perceptually relevant. In this paper we present a new method for selecting
perceptually relevant layers from such a network, based on a neuroscience
interpretation of layer behaviour. The selected layers are treated as a
hyperparameter to the critic network in a W-GAN. The critic uses the output
from these layers in the preliminary stages to extract perceptual information.
A video enhancement network is trained adversarially with this critic. Our
results show that the introduction of these selected features into the critic
yields up to 10% (FID) and 15% (KID) performance increase against other critic
networks that do not exploit the idea of optimised feature selection.
