  Blind Image Quality Assessment (BIQA) is an essential task that estimates the
perceptual quality of images without reference. While many BIQA methods employ
deep neural networks (DNNs) and incorporate saliency detectors to enhance
performance, their large model sizes limit deployment on resource-constrained
devices. To address this challenge, we introduce a novel and non-deep-learning
BIQA method with a lightweight saliency detection module, called Green
Saliency-guided Blind Image Quality Assessment (GSBIQA). It is characterized by
its minimal model size, reduced computational demands, and robust performance.
Experimental results show that the performance of GSBIQA is comparable with
state-of-the-art DL-based methods with significantly lower resource
requirements.
