Image Quality Assessment: Exploring Quality Awareness via Memory-driven Distortion Patterns Matching
Xuting Lan, Mingliang Zhou, Xuekai Wei, Jielu Yan, Yueting Huang, Huayan Pu, Jun Luo, and Weijia Jia

TL;DR
This paper introduces a memory-driven framework for image quality assessment that mimics human visual memory, enabling effective evaluation with or without reference images by matching distortion patterns stored in a memory bank.
Contribution
The proposed MQAF framework is the first to incorporate a memory bank of distortion patterns for adaptive quality assessment in both reference-based and no-reference scenarios.
Findings
Outperforms state-of-the-art methods on multiple datasets.
Effectively switches between reference-guided and no-reference modes.
Achieves high accuracy in real-world applications without high-quality references.
Abstract
Existing full-reference image quality assessment (FR-IQA) methods achieve high-precision evaluation by analysing feature differences between reference and distorted images. However, their performance is constrained by the quality of the reference image, which limits real-world applications where ideal reference sources are unavailable. Notably, the human visual system has the ability to accumulate visual memory, allowing image quality assessment on the basis of long-term memory storage. Inspired by this biological memory mechanism, we propose a memory-driven quality-aware framework (MQAF), which establishes a memory bank for storing distortion patterns and dynamically switches between dual-mode quality assessment strategies to reduce reliance on high-quality reference images. When reference images are available, MQAF obtains reference-guided quality scores by adaptively weighting…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Video Quality Assessment · Image Enhancement Techniques · Advanced Image Processing Techniques
