Differential invariants For Se(2)-Equivariant Networks

Unlike regular images that represent only light intensities, Light Field (LF) contents carry information about the intensity of light in a scene, including the direction in which light rays are traveling in space. This allows for a richer representation of our world, but requires large amounts of data that need to be processed and compressed before being transmitted to the viewer. Since these techniques may introduce distortions, the design of Light Field Image Quality Assessment (LF-IQA) methods is important. Currently, most LF-IQA methods use traditional 2D image quality assessment techniques or rely on low-level spatial features. in this paper, we propose a novel no-reference LF-IQA method that takes into account both LF angular and spatial information. The proposed method is made up of two processing streams with identical blocks of Convolutional Neural Network (CNN), Atrous Convolution layers (ACL), and a regression block for quality prediction. The results show that the method is robust and outperforms current state-of-the-art methods.