Blind omnidirectional image quality assessment: integrating local statistics and global semantics

Zhou, Wei and Wang, Zhou 2023. Blind omnidirectional image quality assessment: integrating local statistics and global semantics. Presented at: International Conference on Image Processing, Kuala Lumpur, Malaysia, 8–11 October 2023. 2023 IEEE International Conference on Image Processing Proceedings. IEEE, pp. 1405-1409. 10.1109/ICIP49359.2023.10222049

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Abstract

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 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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9781728198361
Last Modified:	28 Sep 2023 09:15
URI:	https://orca.cardiff.ac.uk/id/eprint/162498

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