Light field image quality assessment method based on deep graph convolutional neural network
Sana Alamgeer, Muhammad Irshad, Mylène C. Q. Farias · 2022
This paper contains the research proposal of Sana Alamgeer that was presented at the MMSys 2022 doctoral symposium. 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 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. The majority of LF-IQA methods based on traditional Convolutional Neural Network (CNN) have limitations, i.e. they are unable to increase the receptive field of a neuron-pixel to model non-local image features. In this work, we propose a novel no-reference LF-IQA method that is based on Deep Graph Convolutional Neural Network (GCNN). Our method not only takes into account both LF angular and spatial information, but also learns the order of pixel information. Specifically, the method is composed of one input layer that takes a pair of graphs and their corresponding subjective quality scores as labels, 4 GCNN layers, fully connected layers, and a regression block for quality prediction. Our aim is to develop the quality prediction method with maximum accuracy for distorted LF content.