Omnidirectional image quality assessment with mixture-of-experts

We propose a novel mixture-of-experts (MoE) based omnidirectional image quality assessment (OIQA) method, named MoE-OIQA. MoE-OIQA first uses a designed adaptive feature representation module (AFRM) to integrate MoE into multiple stages of a backbone and leverage distortion information extracted from viewports as the prior information for adaptively representing multi-level features of viewports. Then, a quality prediction module (QPM) is designed to obtain the overall quality by weighting the quality scores of individual viewports. Extensive experiments demonstrate that MoE-OIQA effectively improves the model representation capability in diverse distortions and achieves superior performance on both uniform and non-uniform distortions.