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No-reference Image Quality Assessment Leveraging Genai Images.

Home
No-reference Image Quality Assessment Leveraging Genai Images.

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No-Reference Image Quality Assessment Leveraging GenAI Images.

Qingbing Sang, Qian Li, Lixiong Liu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 22, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel no-reference image quality assessment (NR-IQA) method using generative AI (GenAI) images. The approach overcomes data limitations, achieving state-of-the-art performance in image quality evaluation.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Deep learning methods show promise for image quality assessment but struggle with limited real-world data and poor generalization.
Existing challenges in image quality assessment stem from the scarcity of annotated, real-world training datasets.

Purpose of the Study:

To propose a novel no-reference image quality assessment (NR-IQA) method that leverages generative AI (GenAI) images.
To address the limitations of data scarcity and poor generalization in current NR-IQA models.

Main Methods:

Utilized GenAI images as reference images, employing a cold diffusion model to generate distorted images across four distortion types.
Labeled distorted images using a full-reference model to construct a large-scale pre-training dataset for NR-IQA model development.

Integrated a Multi-scale Cross Attention Block (MCAB) and a Scale Simple Attention Module (SSAM) to enhance feature representation by extracting multi-scale information.

Main Results:

The proposed method demonstrated state-of-the-art (SOTA) performance across eight public image quality assessment databases.
The developed large-scale pre-training dataset significantly improved the building of NR-IQA models.
Enhanced feature representation through MCAB and SSAM modules proved effective in predicting image quality.

Conclusions:

The proposed GenAI-based NR-IQA method effectively overcomes data limitations and achieves SOTA performance.
The novel dataset generation and feature extraction techniques offer a promising direction for future NR-IQA research.
The study facilitates the development of more robust and generalizable image quality assessment models.