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Omnidirectional Video Super-Resolution using Deep Learning
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  • Arbind Agrahari Baniya ,
  • Glory Lee ,
  • Peter Eklund ,
  • Sunil Aryal
Arbind Agrahari Baniya
Deakin University, Deakin University

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Glory Lee
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Peter Eklund
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Sunil Aryal
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Omnidirectional Videos (or 360° videos) are widely used in Virtual Reality (VR) to facilitate immersive and interactive viewing experiences. However, the limited spatial resolution in 360° videos does not allow for each degree of view to be represented with adequate pixels, limiting the visual quality offered in the immersive experience. Deep learning Video Super-Resolution (VSR) techniques used for conventional videos could provide a promising software-based solution; however, these techniques do not tackle the distortion present in equirecentagular projections of 360° video signals. An additional obstacle is the limited 360° video datasets to study. To address these issues, this paper creates a novel 360° Video Dataset (360VDS) with a study of the extensibility of conventional VSR models to 360° videos. This paper further proposes a novel deep learning model for 360° Video Super-Resolution (360° VSR), called Spherical Signal Super-resolution with Proportioned Optimisation (S3PO). S3PO adopts recurrent modelling with attention mechanism, unbound from conventional VSR techniques like alignment. With a purpose built feature extractor and a novel loss function addressing spherical distortion, S3PO outperforms most state-of-the-art conventional VSR models and 360° specific super-resolution models on 360° video datasets. A step-wise ablation study is presented to understand and demonstrate the impact of the chosen architectural sub-components, targeted training and optimisation.
2024Published in IEEE Transactions on Multimedia volume 26 on pages 540-554. 10.1109/TMM.2023.3267294