发表机构
University of York（约克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MAV-C无参考框架，融合熵音频特征与视觉特征联合估计视听复杂度，在合成与游戏数据集验证有效性，识别关键可调参数。
AI 中文摘要
我们提出了运动感知视听复杂度度量（MAV-C），一种用于视听复杂度联合客观估计的无参考框架。该度量通过参数化融合阶段，将基于熵的音频特征（时间、频谱和空间）与视觉特征（Sobel梯度幅度、色彩唯一性和光流）相结合，产生连续的联合复杂度评分CAV(t) [0,1]。我们在两个数据集上验证了MAV-C：一个具有已知信号特征的可控合成刺激语料库（SYN）和一个从SAFEPLAY-X数据集提取的60个片段的自然游戏语料库（GAM）。在SYN上，该度量表现出很强的有效性：音频评分CA和视觉评分CV各自对相反模态的变化不敏感（变异系数<0.003），联合评分CAV在所有参数组合上跨越[0.00,0.90]，单轴特征扫描产生单调轨迹（Spearman相关系数最高达0.995）。在GAM上，CV在不同内容类别间存在显著差异（Kruskal-Wallis检验p=0.021），而CA则无显著差异，且两个子评分不相关（r=0.03），确认它们作用于独立的信号维度。单因素敏感性分析（OFAT）识别出两级参数层级，其中模态平衡（wa）和视觉正则化（v）是最重要的可调参数。完整的主观校准计划作为未来工作。
英文摘要
We present the Motion-Aware Audio-Visual Complexity Metric (MAV-C), a reference-free framework for the joint objective estimation of audio-visual complexity. The metric combines entropy-based audio features (temporal, spectral, and spatial) with visual features (Sobel gradient magnitude, chromatic uniqueness, and optical flow) via a parametric fusion stage, producing a continuous joint complexity score CAV (t) [0,1]. We validate MAV-C on two datasets: a controlled synthetic corpus (SYN) of stimuli with known signal characteristics and a naturalistic gameplay corpus (GAM) of 60 clips drawn from the SAFEPLAY-X dataset. On SYN, the metric exhibits strong validity: the audio score CA and visual score CV are each insensitive to changes in the opposite modality (CoV < 0.003), the joint score CAV spans [0.00,0.90] across all parameter combinations, and single-axis feature sweeps produce monotone trajectories (Spearman up to 0.995). On GAM, CV differs significantly across content categories (Kruskal-Wallis p = 0.021) while CA does not, and the two sub-scores are uncorrelated (r = 0.03), confirming they operate on independent signal dimensions. OFAT sensitivity analysis identifies a two-tier parameter hierarchy, with modality balance (wa) and visual regularization (v) as most significant tunable parameters. Full subjective calibration is planned as future work.
CommentsPublished in AES AVARIG 2026: 6th International Conference on Audio for Virtual and Augmented Reality and Immersive Games. Available at: https://aes.org/publications/elibrary-page/?id=23341
Journal refIn Proc. AVARIG 2026: Audio for Virtual and Augmented Reality and Immersive Games; June 2026, pp. 494. Available: https://aes.org/publications/elibrary-page/?id=23341
