GreenBIQA: A Lightweight Blind Image Quality Assessment Method

TL;DR

GreenBIQA is a lightweight, high-performance blind image quality assessment model that uses unsupervised feature extraction and XGBoost, suitable for deployment on mobile and edge devices.

Contribution

It introduces a novel lightweight BIQA approach combining unsupervised feature generation, supervised feature selection, and XGBoost regression for efficient quality prediction.

Findings

Competitive performance with state-of-the-art DNNs

Lower computational complexity and smaller model size

Effective on multiple IQA datasets

Abstract

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.