Perceptual Impact of Facial Quality in MPEG V-PCC-encoded Volumetric Videos
Authors: 
Yuang Shi, 
Wei Tsang OoiAuthors Info & Claims
Pages 71 - 74
Abstract
Volumetric video, a technique used in augmented reality (AR) and virtual reality (VR) applications, presents unique challenges in rendering and compression. To enable efficient compression, video-based point cloud compression (V-PCC) techniques have been introduced by the Moving Picture Experts Group (MPEG). Given the interaction nature of volumetric videos, it is important to understand the impact of user behavior for the optimizations of volumetric video transmission and compression. In this study, we investigate the influence of rendering face quality of the avatars on users' viewing experience in MPEG V-PCC-encoded volumetric videos. We conducted a subjective quality assessment study using the Degradation Category Rating (DCR) method, manipulating facial quality by controlling the compression level of V-PCC. Our analysis reveals the significant role of facial quality in influencing users' overall perceptual quality in volumetric videos. The generated videos and subjective assessment data is made public at https://github.com/nus-vv-streams/facial-quality to support further research.
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Index Terms
- Perceptual Impact of Facial Quality in MPEG V-PCC-encoded Volumetric Videos
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Published In
April 2024
101 pages
ISBN:9798400706189
DOI:10.1145/3652212
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 15 April 2024
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