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Visual attention-aware quality estimation framework for omnidirectional video using spherical Voronoi diagram

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Abstract

Omnidirectional video (ODV) enables viewers to look at every direction from a fixed point and provides a much more immersive experience than traditional 2D video. Assessing the video quality is important for delivering ODV to the end-user with the best possible quality. For this goal, two aspects of ODV should be considered. The first is the spherical nature of ODV and the related projection distortions when the ODV is stored in a planar format. The second is the interactive look-around consumption nature of ODV. Related to this aspect, visual attention, that identifies the regions that attract the viewer’s attention, is important for ODV quality assessment. Considering these aspects, in this paper, we study in particular objective full-reference quality assessment for ODV. To this end, we propose a quality assessment framework based on the spherical Voronoi diagram and visual attention. In this framework, a given ODV is subdivided into multiple planar patches with low projection distortions using the spherical Voronoi diagram. Afterwards, each planar patch is analyzed separately by a quality metric for traditional 2D video, obtaining a quality score for each patch. Then, the patch scores are combined based on visual attention into a final quality score. To validate the proposed framework, we create a dataset of ODVs with scaling and compression distortions, and conduct subjective experiments in order to gather the subjective quality scores and the visual attention data for our ODV dataset. The evaluation of the proposed framework based on our dataset shows that both the use of the spherical Voronoi diagram and visual attention are crucial for achieving state-of-the-art performance.

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Notes

  1. https://v-sense.scss.tcd.ie/research/voronoi-based-objective-metrics/.

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Acknowledgements

This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under the Grant Number 15/RP/2776. This work has also been partially supported by the Ministerio de Economía, Industria y Competitividad (AEI/FEDER) of the Spanish Government under project TEC2016-75981 (IVME)

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Authors and Affiliations

  1. V-SENSE Project, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

    Simone Croci, Cagri Ozcinar, Emin Zerman & Aljosa Smolic

  2. Communication Systems Group, Technical University of Berlin, Berlin, Germany

    Sebastian Knorr

  3. Grupo de Tratamiento de Imágenes, Information Processing and Telecommunications Center and ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain

    Julián Cabrera

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  1. Simone Croci
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Correspondence to Simone Croci.

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Croci, S., Ozcinar, C., Zerman, E. et al. Visual attention-aware quality estimation framework for omnidirectional video using spherical Voronoi diagram. Qual User Exp 5, 4 (2020). https://doi.org/10.1007/s41233-020-00032-3

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