Omnidirectional image quality assessment based on fusion of spatial and gradient domain features

Current deep learning-based omnidirectional image quality assessment (OIQA) methods essentially rely on the global features of omnidirectional images and pay little attention to the local features, and most of them have high computational costs. To solve these problems, a light-weighted model for omnidirectional image quality assessment is presented to accomplish the accurate assessment by extracting and fusing features in spatial and gradient domain. Therefore, spatial attention features are obtained from the spatial information of viewport images as global features, while gradient features are obtained from gradient information as local features. These features are then combined using a Multi-Modal Feature Fusion Network to improve the ability of the model in regards to the feature representation. Experiments were conducted on the two datasets, namely, the CVIQ and OIQA datasets to evaluate the proposed method. The experimental outcomes indicate that the proposed method has high competitiveness in the aspects of performance and model complexity compared with other representative methods.