Modeling User Perception for Multi-Quality Tile-Based 360° Video Streaming

As virtual reality (VR) and 360° video applications continue to gain traction, multi-quality tile-based 360° video streaming has emerged as a flexible and adaptive solution for delivering high-quality immersive content while optimizing bandwidth usage. However, the presence of mixed quality levels within a single 360° video frame can adversely affect users' perceived quality. In this work, we investigate the impact of spatial quality variations across tiles and within users' viewports, and provide improved perceptual quality modeling for tiled 360° videos. To realize this goal, we introduce the Subjective Tile based Assessment for 360° Videos (STAV360) dataset, which includes six source 360° videos, each encoded using twelve tile based configurations, subjective ratings from 27 participants, and corresponding viewing trajectories. Based on this dataset, we propose novel tile-based and viewport-aware 360° video quality assessment (360° VQA) models that incorporate tile-level quality information and user interaction data. Experimental results show that our models outperform conventional full-frame methods by more accurately predicting perceived quality. This work provides key insights for designing perceptually-driven quality metrics and lays the foundation for more efficient and adaptive tile-based 360° video streaming strategies.