Framework predicts audio visual complexity in virtual environments
MAV-C: A Framework for the Joint Objective Estimation of Audio-Visual Complexity in Immersive Virtual Environments
Sound
Summary
This paper introduces a new way to measure how complicated sounds and visuals are together in virtual reality scenes. The method looks at different features like sound patterns and visual motions and combines them into a single score that says how complex the scene is. The authors tested their approach using both made-up examples with known changes and real gameplay clips, finding that their scores reflect differences in visual content well and treat audio and video separately. This tool can help understand and maybe improve how virtual experiences are designed.
What this means in practice
- •For virtual reality developers: Measure and balance audio and visual complexity in VR content to optimize user experience and system performance.
- •For game designers: Analyze complexity levels across gameplay clips to categorize content and tailor difficulty or immersion features.
Authors
Luca Resti, Amelia Gully, Michael McLoughlin, Gavin Kearney, Alena Denisova
Abstract
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.