Omnidirectional image quality assessment using frequency-domain information

Most current omnidirectional image quality assessment (OIQA) models focus on spatial feature representation but rarely consider frequency-domain information. To amend this, we propose a frequency-aware omnidirectional image quality assessment (FOIQA) method that adaptively captures different frequency-domain components. Specifically, we first adaptively decompose an omnidirectional image into high- and low- frequency components and feed the decomposed components to the designed network branches for feature extraction. To enhance the representation of both frequency components, we design different information enhancement modules to enhance high- and low- frequency components, respectively. Then, we consider the mutual influence between local and global perceptions and design a dual-frequency feature fusion module to fuse the enhanced features by simulating the interactions between two frequency components. The fused features are finally used for quality prediction. Experimental results on three public databases show the superiority of our proposed model relative to all compared image quality assessment (IQA) and OIQA models.