Audio-visual quality assessment (AVQA) is essential for streaming, teleconferencing, and immersive media. In realistic streaming scenarios, distortions are often asymmetric, where one modality may be severely degraded while the other remains clean. Still, most contemporary AVQA metrics treat audio and video as equally reliable, causing confidence-unaware fusion to emphasize unreliable signals. This paper proposes MCM-AVQA, a multimodal confidence-aware AVQA framework that explicitly estimates modality-specific confidence and injects it into a dedicated audio-visual mixer for cross-modal attention. The Audio-Visual Mixer utilizes frame-level, confidence-guided channel attention to gate fusion, modulating feature interaction between modalities so that high-confidence streams dominate while unreliable inputs are suppressed, preserving temporal degradation patterns. A multi-head visual confidence estimator turns frame-level artifact probabilities into temporally smoothed, clip-level visual confidence scores, while an audio confidence module derives confidence from speech-quality cues without requiring a clean reference. Experiments on multiple AVQA benchmarks show that MCM-AVQA, and specifically its confidence-guided Audio-Visual Mixer, improve correlation with human mean opinion scores and yield more interpretable behavior under real-world asymmetric audio-visual distortions.

翻译：音视频质量评估（AVQA）对流媒体、远程会议和沉浸式媒体至关重要。在实际流媒体场景中，失真往往呈现非对称性：一种模态可能严重降质，而另一种模态保持清晰。然而，当前大多数AVQA指标将音频与视频视为同等可靠，这种非置信度感知的融合方式会导致不可靠信号被过度强调。本文提出MCM-AVQA——一种多模态置信度感知的AVQA框架，该框架显式估计各模态的特定置信度，并将其注入专用的音视频混合器以实现跨模态注意力机制。音视频混合器利用帧级别、由置信度引导的通道注意力门控融合过程，通过调节模态间的特征交互，使得高置信度流主导融合结果而抑制不可靠输入，同时保持时序退化模式。多头视觉置信度估计器将帧级伪影概率转化为经时序平滑处理的片段级视觉置信度分数，而音频置信度模块则基于语音质量线索推导置信度，无需纯净参考信号。在多个AVQA基准上的实验表明，MCM-AVQA及其置信度引导的音视频混合器能显著提升与人类平均意见分数的相关性，并在真实世界非对称音视频失真条件下展现出更强的可解释性行为。