1. Blind Video Quality Assessment at the Edge
- Author
-
Mei, Zhanxuan, Wang, Yun-Cheng, and Kuo, C. -C. Jay
- Subjects
Image and Video Processing (eess.IV) ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Owing to the proliferation of user-generated videos on the Internet, blind video quality assessment (BVQA) at the edge attracts growing attention. The usage of deep-learning-based methods is restricted by their large model sizes and high computational complexity. In light of this, a novel lightweight BVQA method called GreenBVQA is proposed in this work. GreenBVQA features a small model size, low computational complexity, and high performance. Its processing pipeline includes: video data cropping, unsupervised representation generation, supervised feature selection, and mean-opinion-score (MOS) regression and ensembles. We conduct experimental evaluations on three BVQA datasets and show that GreenBVQA can offer state-of-the-art performance in PLCC and SROCC metrics while demanding significantly smaller model sizes and lower computational complexity. Thus, GreenBVQA is well-suited for edge devices.
- Published
- 2023
- Full Text
- View/download PDF