Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics
This addresses the need for accurate perceptual quality prediction in omnidirectional images, which is crucial for applications like virtual reality, but the approach appears incremental as it builds on existing methods by combining features.
The paper tackled the problem of blind quality assessment for omnidirectional images by integrating local statistics and global semantics, resulting in a method that offers highly competitive performance against state-of-the-art methods.
Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180$\times$360$^{\circ}$ viewing range of the visual environment. Here we propose a blind/no-reference OIQA method named S$^2$ that bridges the gap between low-level statistics and high-level semantics of omnidirectional images. Specifically, statistic and semantic features are extracted in separate paths from multiple local viewports and the hallucinated global omnidirectional image, respectively. A quality regression along with a weighting process is then followed that maps the extracted quality-aware features to a perceptual quality prediction. Experimental results demonstrate that the proposed S$^2$ method offers highly competitive performance against state-of-the-art methods.