BLIND OMNIDIRECTIONAL IMAGE QUALITY ASSESSMENT: INTEGRATING LOCAL STATISTICS AND GLOBAL SEMANTICS

Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180x360 degrees viewing range of the visual environment. Here we propose a blind/no-reference OIQA method named Local Statistics and Global Semantics metric (LSGS) 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 LSGS method offers highly competitive performance against state-of-the-art methods.