No-Reference Deep Quality Assessment of Compressed Light Field Images
Zixuan Guo, Wei Gao, Haiqiang Wang, Junle Wang, Songlin Fan
Abstract
Unlike traditional 2D image quality assessment, the structural relationship among sub-aperture images (SAIs) is an essential factor affecting the quality evaluation of light field (LF) images, where the labeled datasets are also not sufficient for improving learning performances. To solve these problems, we present a novel deep neural network-based approach to accurately predict the quality of compressed LF images without pristine images. Two modules dubbed SAI-Fusion and Global Context Perception (GCP) are proposed to obtain the relationship among SAIs. For effective training, we compress LF images from EPFL and HCI datasets and propose a ranking-based method to generate pseudo-labels as equivalents of Mean Opinion Score (MOS), i.e., Ranking-MOS. Therefore, we can pre-train our quality assessment network on compressed LF images with Ranking-MOS, and then fine-tune the model at small-scale datasets with real labels. Experiments demonstrate that the proposed method achieves state-of-the-art performance on compressed LF images of Win5-LID dataset.