Subjective and Objective Audio-Visual Quality Assessment for Omnidirectional Videos
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new method for assessing the quality of audio-visual content in virtual reality (VR). The developed OmniAVNet model effectively predicts the overall quality of omnidirectional videos (ODVs), improving user experience.
Area Of Science
- Computer Science
- Multimedia Systems
- Signal Processing
Background
- Virtual Reality (VR) and Omnidirectional Videos (ODVs) are increasingly popular for immersive experiences.
- Existing Quality of Experience (QoE) studies for ODVs primarily focus on visual aspects, neglecting audio's impact.
- Optimizing ODV quality across production and transmission is crucial for user satisfaction.
Purpose Of The Study
- To comprehensively study omnidirectional audio-visual quality assessment (OD-AVQA).
- To develop a large-scale database for OD-AVQA.
- To propose and validate novel objective OD-AVQA models.
Main Methods
- Established OAVQAD+, the largest database for ODV audio-visual quality assessment, with 625 distorted sequences and MOS scores.
- Constructed a benchmark including Type I, II, and III OD-AVQA models.
- Proposed OmniAVNet, a novel network integrating audio, visual, and motion features for full-reference (FR) and no-reference (NR) OD-AVQA.
Main Results
- OmniAVNet significantly outperforms existing benchmark OD-AVQA models on multiple datasets.
- The proposed model demonstrates strong performance in predicting audio-visual quality for ODVs.
- The study provides valuable resources (database and code) for advancing OD-AVQA research.
Conclusions
- Audio modality significantly impacts the perceived quality of ODVs.
- OmniAVNet offers an effective solution for objective OD-AVQA, supporting both FR and NR modes.
- This work contributes to enhancing the QoE of immersive VR experiences through better ODV quality assessment.
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