  Existing deep learning-based full-reference IQA (FR-IQA) models usually
predict the image quality in a deterministic way by explicitly comparing the
features, gauging how severely distorted an image is by how far the
corresponding feature lies from the space of the reference images. Herein, we
look at this problem from a different viewpoint and propose to model the
quality degradation in perceptual space from a statistical distribution
perspective. As such, the quality is measured based upon the Wasserstein
distance in the deep feature domain. More specifically, the 1DWasserstein
distance at each stage of the pre-trained VGG network is measured, based on
which the final quality score is performed. The deep Wasserstein distance
(DeepWSD) performed on features from neural networks enjoys better
interpretability of the quality contamination caused by various types of
distortions and presents an advanced quality prediction capability. Extensive
experiments and theoretical analysis show the superiority of the proposed
DeepWSD in terms of both quality prediction and optimization.
