No-Reference Video Quality Assessment with Heterogeneous Knowledge Ensemble
Jinjian Wu, Yongxu Liu, Leida Li, Weisheng Dong, Guangming Shi · 2021
Blind assessment of video quality is still challenging even in this deep learning era. The limited number of samples in existing databases is insufficient to learn a good feature extractor for video quality assessment (VQA), while manually labeling a larger database with subjective perception is very labor-intensive and time-consuming. To relieve such difficulty, we first collect 3589 high-quality video clips as the reference and build a large VQA dataset. The dataset contains more than 300K samples degraded by various distortion types due to compression and transmission error, and provides weak labels for each distorted sample with several full-reference VQA algorithms. To learn effective representation from the weakly labeled data, we alleviate the bias of single weak label (i.e., single knowledge) via learning from multiple heterogeneous knowledge. To this end, we propose a novel no-reference VQA (NR-VQA) method with HEterogeneous Knowledge Ensemble (HEKE). Comparing to learning from single knowledge, HEKE can theoretically reach a lower infimum, and learn richer representation due to the heterogeneity. Extensive experimental results show that the proposed HEKE outperforms existing NR-VQA methods, and achieves the state-of-the-art performance. The source code will be available at https://github.com/Sissuire/BVQA-HEKE.