Improving Spherical Image Resampling through Viewport-Adaptivity

TL;DR

This paper introduces a viewport-adaptive resampling (VAR) method for spherical images that leverages their unique properties, significantly improving resampling quality across various algorithms and outperforming traditional methods in quality metrics.

Contribution

The paper presents a novel viewport-adaptive resampling technique specifically designed for spherical images, enhancing existing resampling algorithms by exploiting spherical characteristics.

Findings

VAR improves resampling quality across tested algorithms.

Outperforms conventional cubic resampling by over 2 dB in WS-PSNR.

Supports visual and metric-based quality improvements.

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.