Hierarchical Feedback for No-reference Video Quality Assessment Using a Spatiotemporal Feature Pyramid
Year: 2026
Type: conference-paper
Abstract: No-reference video quality assessment (NR-VQA) is challenging due to large content variability and complex real-world distortions. Existing deep NR-VQA approaches mainly rely on feedforward fusion of multi-scale spatiotemporal features, which can struggle to disentangle distortion from content. We propose a hierarchical feedback NR-VQA framework built on a generic spatiotemporal encoder and a feature pyramid network (FPN)-style hierarchy. Multi-level features are extracted from video clips and combined to capture distortions at different spatial and temporal scales. Global high-level context is then fed back to refine lower-level representations via lightweight channel-wise gating, enhancing distortion sensitivity and reducing content bias. Clip-level predictions are aggregated to obtain video quality scores. Experiments on multiple representative UGC-VQA databases demonstrate competitive performance, and ablation studies validate the benefits of hierarchical feedback over feedforward multi-scale baselines.
Authors: Reshu Bansal, Parimala Kancharla
Institution: Indian Institute of Technology Mandi
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7202380468