ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images
Weiqi Li, Shijie Zhao, Bin Chen, Xinhua Cheng, Junlin Li, Li Zhang,, Jian Zhang

TL;DR
ResVR introduces a novel framework for joint rescaling and viewport rendering of omnidirectional images, improving user viewport quality in virtual reality while reducing transmission size.
Contribution
It is the first comprehensive approach combining rescaling and viewport rendering for ODIs, with a novel pixel sampling and spherical shape representation techniques.
Findings
Outperforms existing viewport rendering methods in quality.
Maintains low transmission overhead.
Effective across various FOVs, resolutions, and view directions.
Abstract
With the advent of virtual reality technology, omnidirectional image (ODI) rescaling techniques are increasingly embraced for reducing transmitted and stored file sizes while preserving high image quality. Despite this progress, current ODI rescaling methods predominantly focus on enhancing the quality of images in equirectangular projection (ERP) format, which overlooks the fact that the content viewed on head mounted displays (HMDs) is actually a rendered viewport instead of an ERP image. In this work, we emphasize that focusing solely on ERP quality results in inferior viewport visual experiences for users. Thus, we propose ResVR, which is the first comprehensive framework for the joint Rescaling and Viewport Rendering of ODIs. ResVR allows obtaining LR ERP images for transmission while rendering high-quality viewports for users to watch on HMDs. In our ResVR, a novel discrete pixel…
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Taxonomy
TopicsAdvanced Vision and Imaging · Medical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques
MethodsFocus
