对无向视频的主观和客观视听质量评估
概括
本研究引入了一种用于评估虚拟现实 (VR) 中视听内容质量的新方法. 开发的OmniAVNet模型有效地预测了全方位视频 (ODV) 的整体质量,改善了用户体验.
科学领域:
- 计算机科学 计算机科学
- 多媒体系统 多媒体系统
- 信号处理 信号处理
背景情况:
- 虚拟现实 (VR) 和全方位视频 (ODV) 对于沉浸式体验越来越受欢迎.
- 现有的ODV体验质量 (QoE) 研究主要集中在视觉方面,忽视了音频的影响.
- 在生产和传输中优化ODV质量对于用户满意度至关重要.
研究的目的:
- 综合研究全方位视听质量评估 (OD-AVQA).
- 为OD-AVQA.开发一个大规模的数据库.
- 提出和验证新的客观OD-AVQA模型.
主要方法:
- 建立了OAVQAD+,这是最大的ODV视听质量评估数据库,拥有625个扭曲序列和MOS分数.
- 构建了一个基准,包括I型,II型和III型OD-AVQA模型.
- 提出了OmniAVNet,这是一个集成音频,视觉和运动特征的新型网络,用于完全引用 (FR) 和无引用 (NR) OD-AVQA.
主要成果:
- 在多个数据集上,OmniAVNet显著优于现有的基准OD-AVQA模型.
- 拟议的模型在预测ODV的视听质量方面表现强.
- 该研究为推进OD-AVQA研究提供了宝贵的资源 (数据库和代码).
结论:
- 音频模式显著影响ODV的感知质量.
- OmniAVNet为客观的OD-AVQA提供了一个有效的解决方案,支持FR和NR模式.
- 这项工作有助于通过更好的ODV质量评估来提高沉浸式VR体验的QOE.
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