GSBIQA: Green Saliency-guided Blind Image Quality Assessment Method

TL;DR

GSBIQA is a lightweight, non-deep-learning blind image quality assessment method that achieves competitive performance with minimal computational resources, suitable for deployment on resource-constrained devices.

Contribution

It introduces a novel, resource-efficient BIQA approach with a lightweight saliency detection module, reducing model size and computational demands.

Findings

Performance comparable to state-of-the-art DL-based methods

Significantly lower resource requirements

Minimal model size and computational demands

Abstract

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.