Spatial-angular Quality-aware Representation Learning for Blind Light Field Image Quality Assessment

Blind light field image quality assessment (BLFIQA) remains a challenging task in deep learning due to the unique spatial-angular structure of light field images (LFIs) and the lack of large-scale labeled data for training. In this work, we propose a novel BLFIQA method using spatial-angular quality-aware representation learning in a self-supervised learning manner. Visual content and distortion type are important factors affecting the perceived quality of LFIs. In our observation, the band-pass transform maps of LFIs with the same distortion type exhibit similar Gaussian distributions. Thus, we learn spatial-angular quality-aware representations by minimizing the distance in the embedding space between the luminance map and the band-pass transform map of the same LFI. To implement spatial-angular quality-aware representations of LFI, we also build a large-scale unlabeled dataset containing 40k distorted LFIs with different distortion types and visual content. Further, we propose a fusion-separation-fusion network (FSFNet) to extract features for representing the intrinsic spatial-angular structure of the LFI. After pre-training on the unlabeled dataset using the proposed self-supervised learning, the FSFNet is employed for downstream BLFIQA tasks and achieves good performance. Experimental results show that our proposed method outperforms seventeen state-of-the-art models on the Win5-LID, NBU-LF1.0 and LFDD datasets, and achieves 3.78%, 6.61% and 4.06% SRCC improvements, respectively. The code and dataset will be publicly available in https://github.com/JianjunXiang/SSL_and_FSFNet.