  Blind image quality assessment (BIQA) is a task that predicts the perceptual
quality of an image without its reference. Research on BIQA attracts growing
attention due to the increasing amount of user-generated images and emerging
mobile applications where reference images are unavailable. The problem is
challenging due to the wide range of content and mixed distortion types. Many
existing BIQA methods use deep neural networks (DNNs) to achieve high
performance. However, their large model sizes hinder their applicability to
edge or mobile devices. To meet the need, a novel BIQA method with a small
model, low computational complexity, and high performance is proposed and named
"GreenBIQA" in this work. GreenBIQA includes five steps: 1) image cropping, 2)
unsupervised representation generation, 3) supervised feature selection, 4)
distortion-specific prediction, and 5) regression and decision ensemble.
Experimental results show that the performance of GreenBIQA is comparable with
that of state-of-the-art deep-learning (DL) solutions while demanding a much
smaller model size and significantly lower computational complexity.
