Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling
This work addresses online video quality assessment (VQA), targeting the fundamental limits of spatiotemporal redundancy compression to achieve optimal trade-offs between efficiency and accuracy. We propose a joint spatiotemporal sampling strategy that reduces spatial resolution and frame rate to ≤10% of the original while incurring <8% performance degradation. The method comprises a lightweight spatial feature extractor, an efficient temporal fusion module, and a global quality regression network—forming a low-latency, real-time-capable VQA architecture. To our knowledge, this is the first study to systematically characterize the performance tolerance thresholds for spatiotemporal compression in VQA. Extensive experiments on six mainstream public benchmarks demonstrate strong generalization and robustness. Our approach establishes the first practical, high-accuracy, low-overhead solution for real-time VQA in edge-computing and streaming scenarios.