Multi-Task Guided No-Reference Omnidirectional Image Quality Assessment With Feature Interaction

Abstract

Omnidirectional image quality assessment (OIQA) has become an increasingly vital problem in recent years. Most previous no-reference OIQA methods only extract local features from the distorted viewports, or extract global features from the entire distorted image, lacking the interaction and fusion between local and global features. Moreover, the lack of reference information also limits their performance. Thus, we propose a no-reference OIQA model which consists of three novel modules, including a bidirectional pseudo-reference module, a Mamba-based global feature extraction module, and a multi-scale local-global feature aggregation module. Specifically, by considering the image distortion degradation process, a bidirectional pseudo-reference module capturing the error maps on viewports is first constructed to refine the multi-scale local visual features, which can supply rich quality degradation reference information without the reference image. To well complement the local features, the VMamba module is adopted to extract the representative multi-scale global visual features. Inspired by human hierarchical visual perception characteristics, a novel multi-scale aggregation module is built to strengthen the feature interaction and effective fusion which can extract deep semantic information. Finally, motivated by the multi-task managing mechanism of human brain, a multi-task learning module is introduced to assist the main quality assessment task by digging the hidden information in compression type and distortion degree. Extensive experimental results demonstrate that our proposed method achieves the state-of-the-art performance on the no-reference OIQA task compared to other models.