Frequency-aware native resolution assessment of 8K omnidirectional images

Hou, Jingwen, Li, Zengliang, Yan, Jiebin, Liu, Weide, Fang, Yuming and Zhou, Wei 2025. Frequency-aware native resolution assessment of 8K omnidirectional images. Presented at: 2025 International Conference on Visual Communications and Image Processing (VCIP), Klagenfurt, Austria, 1-4 December 2025. 2025 International Conference on Visual Communications and Image Processing (VCIP). 2025 International Conference on Visual Communications and Image Processing (VCIP). IEEE, 10.1109/vcip67698.2025.11396856

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Abstract

Omnidirectional images (ODIs) serve as fundamental visual medium for presenting virtual reality (VR) contents, supporting fully immersive experiences through 360-degree scene representation. Typically, a high pixel density is essential for visual quality in VR environments, which in turn requires sufficiently high-resolution imagery to achieve. However, capturing native high-resolution ODIs requires expensive omnidirectional cameras with large sensors (e.g., Insta360 TITAN). An alternative approach is to use low-resolution cameras to acquire original images and then enhance their resolution via super-resolution algorithms. In this work, we explore whether super-resolution ODIs can be easily distinguished from native high-resolution ODIs at 8K scale. To this end, we firstly construct the Native Resolution Assessment of 8K Omnidirectional Images (NRA- 8KODI) dataset, whose native 8K ODIs are collected with an Insta360 TITAN camera and 8K super-resolution images are generated from SOTA open-sourced algorithms. Recognizing high-frequency signals are essential for differentiating non-native 8K ODIs, a frequency-aware model is designed to capture high-frequency details. Specially, to maintain high-frequency details kept in high-resolutions while reduce computational costs brought by high-resolutions, we propose a frequency-aware compressor module to suppress feature channels dominated by low-frequency details. Finally, our model achieves 97.2% accuracy in detecting non-native 8K ODIs, implying that super-resolution for ODIs can still be improved for visual experience in VR applications.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	979-8-3315-6867-2
ISSN:	2642-9357
Last Modified:	13 Mar 2026 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/185728

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