MAFBLiF: Multi-Scale Attention Feature Fusion-Based Blind Light Field Image Quality Assessment

Light field imaging captures both the intensity and directional information of light rays, providing users with more immersive visual experience. However, during the processes of imaging, processing, coding and reconstruction, light field images (LFIs) may encounter various distortions that degrade their visual quality. Compared to two-dimensional image quality assessment, light field image quality assessment (LFIQA) needs to consider not only the image quality in the spatial domain but also the quality degradation in the angular domain. To effectively model the factors related to visual perception and LFI quality, this paper proposes a multi-scale attention feature fusion based blind LFIQA metric, named MAFBLiF. The proposed metric consists of the following parts: MLI-Patch generation, spatial-angular feature separation module, spatial-angular feature extraction backbone network, pyramid feature alignment module and patch attention module. These modules are specifically designed to extract spatial and angular information of LFIs, and capture multi-level information and regions of interest. Furthermore, a pooling scheme guided by the LFI's gradient information and saliency is proposed, which integrates the quality of all MLI-patches into the overall quality of the input LFI. Finally, to demonstrate the effectiveness of the proposed metric, extensive experiments are conducted on three representative LFI quality evaluation datasets. The experimental results show that the proposed metric outperforms other state-of-the-art image quality assessment metrics. The code will be publicly available at https://github.com/oldblackfish/MAFBLiF.