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.

翻译：基于图像的深度特征质量指标（DFQM）已被证明比传统指标能更好地与主观感知评分相关。这些DFQM的核心在于利用大规模分类网络的内部表征作为度量特征空间。此前，尚未有研究关注如何识别在感知上最相关的网络层这一问题。本文提出一种新方法，基于对网络层行为的神经科学解释，从此类网络中选取感知相关层。所选层被作为W-GAN中评判器网络的超参数。该评判器在初始阶段利用这些层的输出提取感知信息。一个视频增强网络通过与此评判器进行对抗训练而得到优化。结果表明，与未采用优化特征选择理念的其他评判器网络相比，将所选特征引入评判器后，性能在FID上提升高达10%，在KID上提升高达15%。