Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images

• URL: http://arxiv.org/abs/2501.11511v1
• Date: Mon, 20 Jan 2025 14:39:50 GMT
• Title: Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images
• Authors: Jiebin Yan, Jiale Rao, Xuelin Liu, Yuming Fang, Yifan Zuo, Weide Liu,
• Abstract summary: We propose a perception-guided OIQA model for non-uniform distortion by adaptively simulating users' viewing behavior. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods.
• Score: 33.10692798685548
• License:
• Abstract: Omnidirectional image quality assessment (OIQA) has been one of the hot topics in IQA with the continuous development of VR techniques, and achieved much success in the past few years. However, most studies devote themselves to the uniform distortion issue, i.e., all regions of an omnidirectional image are perturbed by the ``same amount'' of noise, while ignoring the non-uniform distortion issue, i.e., partial regions undergo ``different amount'' of perturbation with the other regions in the same omnidirectional image. Additionally, nearly all OIQA models are verified on the platforms containing a limited number of samples, which largely increases the over-fitting risk and therefore impedes the development of OIQA. To alleviate these issues, we elaborately explore this topic from both subjective and objective perspectives. Specifically, we construct a large OIQA database containing 10,320 non-uniformly distorted omnidirectional images, each of which is generated by considering quality impairments on one or two camera len(s). Then we meticulously conduct psychophysical experiments and delve into the influence of both holistic and individual factors (i.e., distortion range and viewing condition) on omnidirectional image quality. Furthermore, we propose a perception-guided OIQA model for non-uniform distortion by adaptively simulating users' viewing behavior. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods. The source code is available at https://github.com/RJL2000/OIQAND.

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