  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.
