Geometric Transformation Invariant Image Quality Assessment Using Convolutional Neural Networks

Abstract

Most existing full-reference (FR) image quality assessment (IQA) models assume that the reference and distorted images are perfectly aligned, and fail dramatically when the assumption does not hold. In this study, we first show that pre-registration, especially feature-based (as opposed to area-based) registration, is effective at reducing the performance drop of FR-IQA models. However, registration is an expensive process that often slows down the speed of the IQA algorithms by several orders of magnitude. This motivates us to construct an end-to-end convolutional neural network (CNN) for direct image quality prediction, which contains built-in invariance to geometric distortions. Our results show that when the training images are augmented by their geometrically transformed versions, the learned network performs at a high level without image registration, resulting in a fast and effective approach for geometric transformation invariant IQA.