Frontiers in Neuroscience (Nov 2022)

Dynamically attentive viewport sequence for no-reference quality assessment of omnidirectional images

  • Yuhong Wang,
  • Yuhong Wang,
  • Hong Li,
  • Qiuping Jiang

DOI
https://doi.org/10.3389/fnins.2022.1022041
Journal volume & issue
Vol. 16

Abstract

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Omnidirectional images (ODIs) have drawn great attention in virtual reality (VR) due to the capability of providing an immersive experience to users. However, ODIs are usually subject to various quality degradations during different processing stages. Thus, the quality assessment of ODIs is of critical importance to the community of VR. The quality assessment of ODIs is quite different from that of traditional 2D images. Existing IQA methods focus on extracting features from spherical scenes while ignoring the characteristics of actual viewing behavior of humans in continuously browsing an ODI through HMD and failing to characterize the temporal dynamics of the browsing process in terms of the temporal order of viewports. In this article, we resort to the law of gravity to detect the dynamically attentive regions of humans when viewing ODIs. In this article, we propose a novel no-reference (NR) ODI quality evaluation method by making efforts on two aspects including the construction of Dynamically Attentive Viewport Sequence (DAVS) from ODIs and the extraction of Quality-Aware Features (QAFs) from DAVS. The construction of DAVS aims to build a sequence of viewports that are likely to be explored by viewers based on the prediction of visual scanpath when viewers are freely exploring the ODI within the exploration time via HMD. A DAVS that contains only global motion can then be obtained by sampling a series of viewports from the ODI along the predicted visual scanpath. The subsequent quality evaluation of ODIs is performed merely based on the DAVS. The extraction of QAFs aims to obtain effective feature representations that are highly discriminative in terms of perceived distortion and visual quality. Finally, we can adopt a regression model to map the extracted QAFs to a single predicted quality score. Experimental results on two datasets demonstrate that the proposed method is able to deliver state-of-the-art performance.

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