A no-reference video quality predictor for compression and scaling artifacts
Deepti Ghadiyaram, Chao Chen, Sasi Inguva, Anil C. Kokaram · 2017
No-Reference (NR) video quality assessment (VQA) models are gaining popularity as they offer scope for broader applicability to user-uploaded video-centric services such as YouTube and Facebook, where the pristine references are unavailable. However, there are few, well-performing NR-VQA models owing to the difficulty of the problem. We propose a novel NR video quality predictor that solely relies on the `quality-aware' natural statistical models in the space-time domain. The proposed quality predictor called Self-reference based LEarning-free Evaluator of Quality (SLEEQ) consists of three components: feature extraction in the spatial and temporal domains, motion-based feature fusion, and spatial-temporal feature pooling to derive a single quality score for a given video. SLEEQ achieves higher than 0.9 correlation with the subjective video quality scores on tested public databases and thus outperforms the existing NR VQA models.