Deep neural networks (DNNs) achieve great success in blind image quality assessment (BIQA) with large pre-trained models in recent years. Their solutions cannot be easily deployed at mobile or edge devices, and a lightweight solution is desired. In this work, we propose a novel BIQA model, called GreenBIQA, that aims at high performance, low computational complexity and a small model size. GreenBIQA adopts an unsupervised feature generation method and a supervised feature selection method to extract quality-aware features. Then, it trains an XGBoost regressor to predict quality scores of test images. We conduct experiments on four popular IQA datasets, which include two synthetic-distortion and two authentic-distortion datasets. Experimental results show that GreenBIQA is competitive in performance against state-of-the-art DNNs with lower complexity and smaller model sizes.
@article{arxiv.2206.14400,
title = {GreenBIQA: A Lightweight Blind Image Quality Assessment Method},
author = {Zhanxuan Mei and Yun-Cheng Wang and Xingze He and C. -C. Jay Kuo},
journal= {arXiv preprint arXiv:2206.14400},
year = {2022}
}