1 research outputs found
Disentangling Image Distortions in Deep Feature Space
Previous literature suggests that perceptual similarity is an emergent
property shared across deep visual representations. Experiments conducted on a
dataset of human-judged image distortions have proven that deep features
outperform classic perceptual metrics. In this work we take a further step in
the direction of a broader understanding of such property by analyzing the
capability of deep visual representations to intrinsically characterize
different types of image distortions. To this end, we firstly generate a number
of synthetically distorted images and then we analyze the features extracted by
different layers of different Deep Neural Networks. We observe that a
dimension-reduced representation of the features extracted from a given layer
permits to efficiently separate types of distortions in the feature space.
Moreover, each network layer exhibits a different ability to separate between
different types of distortions, and this ability varies according to the
network architecture. Finally, we evaluate the exploitation of features taken
from the layer that better separates image distortions for: i)
reduced-reference image quality assessment, and ii) distortion types and
severity levels characterization on both single and multiple distortion
databases. Results achieved on both tasks suggest that deep visual
representations can be unsupervisedly employed to efficiently characterize
various image distortions