MetaQAP -- A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessment

2025-06-19

AI Summary

To address the limited generalizability and robustness of no-reference image quality assessment (NR-IQA) models—stemming from strong subjectivity in human perception and the complexity of real-world distortions—this paper proposes a quality-aware pretraining and meta-learning synergistic framework. The framework uniquely integrates quality-oriented self-supervised pretraining, a customized quality-aware loss function, and a meta-learning-driven multi-model ensemble mechanism. Built upon a CNN-based feature extractor, it is jointly trained and validated on multiple benchmark datasets: LIVECD, KonIQ-10K, and BIQ2021. Experimental results demonstrate state-of-the-art performance: on in-distribution datasets, it achieves PLCC scores of 0.9885/0.9702/0.884 and SROCC scores of 0.9812/0.9658/0.8765; in cross-dataset evaluation, it attains PLCC/SROCC ranging from 0.6721–0.8023 / 0.6515–0.7805—significantly outperforming existing methods. The framework substantially enhances model robustness to authentic distortions and cross-domain generalization capability.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Image Quality Assessment (IQA) is a critical task in a wide range of applications but remains challenging due to the subjective nature of human perception and the complexity of real-world image distortions. This study proposes MetaQAP, a novel no-reference IQA model designed to address these challenges by leveraging quality-aware pre-training and meta-learning. The model performs three key contributions: pre-training Convolutional Neural Networks (CNNs) on a quality-aware dataset, implementing a quality-aware loss function to optimize predictions, and integrating a meta-learner to form an ensemble model that effectively combines predictions from multiple base models. Experimental evaluations were conducted on three benchmark datasets: LiveCD, KonIQ-10K, and BIQ2021. The proposed MetaQAP model achieved exceptional performance with Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) scores of 0.9885/0.9812 on LiveCD, 0.9702/0.9658 on KonIQ-10K, and 0.884/0.8765 on BIQ2021, outperforming existing IQA methods. Cross-dataset evaluations further demonstrated the generalizability of the model, with PLCC and SROCC scores ranging from 0.6721 to 0.8023 and 0.6515 to 0.7805, respectively, across diverse datasets. The ablation study confirmed the significance of each model component, revealing substantial performance degradation when critical elements such as the meta-learner or quality-aware loss function were omitted. MetaQAP not only addresses the complexities of authentic distortions but also establishes a robust and generalizable framework for practical IQA applications. By advancing the state-of-the-art in no-reference IQA, this research provides valuable insights and methodologies for future improvements and extensions in the field.

Problem

• Addressing subjective human perception in image quality assessment
• Overcoming complexity of real-world image distortions in IQA
• Enhancing generalizability of no-reference IQA models

Innovation

• Quality-aware pre-training for CNNs
• Meta-learner for ensemble model integration
• Quality-aware loss function optimization