Attentive AV-FusionNet: Audio-Visual Quality Prediction with Hybrid Attention

Ina Salaj; Arijit Biswas

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arXiv · 2509.16994

Attentive AV-FusionNet: Audio-Visual Quality Prediction with Hybrid Attention

Ina Salaj·Arijit Biswas

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Abstract

We introduce a novel deep learning-based audio-visual quality (AVQ) prediction model that leverages internal features from state-of-the-art unimodal predictors. Unlike prior approaches that rely on simple fusion strategies, our model employs a hybrid representation that combines learned Generative Machine Listener (GML) audio features with hand-crafted Video Multimethod Assessment Fusion (VMAF) video features. Attention mechanisms capture cross-modal interactions and intra-modal relationships, yielding context-aware quality representations. A modality relevance estimator quantifies each modality's contribution per content, potentially enabling adaptive bitrate allocation. Experiments demonstrate improved AVQ prediction accuracy and robustness across diverse content types.

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BibTeX RIS

Ina Salaj, Arijit Biswas. 2025-09-21. Attentive AV-FusionNet: Audio-Visual Quality Prediction with Hybrid Attention. https://arxiv.org/abs/2509.16994

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