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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, Weijia Jia

TL;DR

A memory-driven quality-aware framework (MQAF) is proposed, 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.

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 reference information and comparing the distorted image with stored distortion patterns in the memory bank. When the reference image is absent, the framework relies on distortion patterns in the memory bank to infer image quality, enabling no-reference quality assessment (NR-IQA). The experimental results show that our method outperforms state-of-the-art approaches across multiple datasets while adapting to both no-reference and full-reference tasks.

Image Quality Assessment: Exploring Quality Awareness via Memory-driven Distortion Patterns Matching

TL;DR

A memory-driven quality-aware framework (MQAF) is proposed, 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.

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 reference information and comparing the distorted image with stored distortion patterns in the memory bank. When the reference image is absent, the framework relies on distortion patterns in the memory bank to infer image quality, enabling no-reference quality assessment (NR-IQA). The experimental results show that our method outperforms state-of-the-art approaches across multiple datasets while adapting to both no-reference and full-reference tasks.
Paper Structure (21 sections, 12 equations, 4 figures, 10 tables)

This paper contains 21 sections, 12 equations, 4 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Image Quality Assessment
  4. Deep feature-based image quality assessment
  5. Problem Formulation
  6. Methodology
  7. Framework
  8. Memory-driven Distortion Pattern Storage and Matching Metric
  9. Connection to the HVS and existing methods
  10. Experiments
  11. Experimental setups
  12. Experimental Results
  13. Comparison with the State-of-the-Art Method
  14. Performance Evaluation Cross Different Databases
  15. Performance Evaluation on Individual Distortion Types
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Comparison of MQAF with existing IQA methods, highlighting the exceptional IQA ability of MQAF-R.
  • Figure 2: The framework (MQAF) of our proposed method can adaptively select different modes based on the availability of reference images in the quality assessment process. When a reference image is available, the framework enters the reference mode, where the final quality score $Q$ is obtained by computing reference matching and memory matching. When no reference image is available, the framework switches to the no-reference mode, where the final quality score $Q$ is determined by matching with distortion patterns (memory units $\mathcal{V}$) stored in the memory bank.
  • Figure 3: Scatter plot of prediction results for different FR-IQA methods and our method (MQAF-R) on the KADID-10K kadid dataset. The vertical axis represents the ground truth values, whereas the horizontal axis represents the predicted values.
  • Figure 4: Comparison of gMAD competition results on the TID2013 dataset among our proposed MQAF-R method, PieAPP pieapp, WaDIQaM-FR WaDIQaM-FR, and DISTS DISTS is presented.