Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics

• URL: http://arxiv.org/abs/2302.12393v1
• Date: Fri, 24 Feb 2023 01:47:13 GMT
• Title: Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics
• Authors: Wei Zhou and Zhou Wang
• Abstract summary: 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. A quality regression along with a weighting process is then followed that maps the extracted quality-aware features to a perceptual quality prediction.
• Score: 14.586878663223832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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