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2025 | OriginalPaper | Chapter

PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts

Authors : Zewen Chen, Haina Qin, Juan Wang, Chunfeng Yuan, Bing Li, Weiming Hu, Liang Wang

Published in: Computer Vision – ECCV 2024

Publisher: Springer Nature Switzerland

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Abstract

The chapter discusses the challenges of No-Reference Image Quality Assessment (NR-IQA) and introduces PromptIQA, a novel framework that adapts to new assessment requirements using image-score pairs as prompts. The method enhances model performance and generalization without the need for extensive datasets or fine-tuning. Experiments demonstrate the effectiveness of PromptIQA in mixed training scenarios, highlighting its ability to learn and adapt to various assessment criteria. The chapter also includes detailed discussions on data augmentation strategies and the impact of different prompt selection methods, providing a comprehensive overview of the proposed approach.
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Metadata
Title
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts
Authors
Zewen Chen
Haina Qin
Juan Wang
Chunfeng Yuan
Bing Li
Weiming Hu
Liang Wang
Copyright Year
2025
Book
Computer Vision – ECCV 2024

Print ISBN: 978-3-031-73231-7

Electronic ISBN: 978-3-031-73232-4

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-73232-4_14

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