Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment

Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180×360 • 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.