Improving Spherical Image Resampling through Viewport-Adaptivity

Regensky A, Heimann V, Zhang R, Kaup A (2023)

Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Pages Range: 1730-1734

Event location: Kuala Lumpur

URI: https://arxiv.org/abs/2306.13692

DOI: 10.1109/ICIP49359.2023.10222645

Abstract

The conversion between different spherical image and video projection formats requires highly accurate resampling techniques in order to minimize the inevitable loss of information. Suitable resampling algorithms such as nearest neighbor, linear or cubic resampling are readily available. However, no generally applicable resampling technique exploits the special properties of spherical images so far. Thus, we propose a novel viewport-adaptive resampling (VAR) technique that takes the spherical characteristics of the underlying resampling problem into account. VAR can be applied to any mesh-to-mesh capable resampling algorithm and shows significant gains across all tested techniques. In combination with frequency-selective resampling, VAR outperforms conventional cubic resampling by more than 2 dB in terms of WS-PSNR. A visual inspection and the evaluation of further metrics such as PSNR and SSIM support the positive results.

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How to cite

APA:

Regensky, A., Heimann, V., Zhang, R., & Kaup, A. (2023). Improving Spherical Image Resampling through Viewport-Adaptivity. In Proceedings of the IEEE International Conference on Image Processing (ICIP) (pp. 1730-1734). Kuala Lumpur, MY.

MLA:

Regensky, Andy, et al. "Improving Spherical Image Resampling through Viewport-Adaptivity." Proceedings of the IEEE International Conference on Image Processing (ICIP), Kuala Lumpur 2023. 1730-1734.

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