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EVALUATING NON-REFERENCE IMAGE QUALITY METRICS FOR AI-GENERATED IMAGES

AUTHORS:
B. Midhun Kannan
K. Dhanalakshmi
Mentor
Affiliation
Department of Artificial Intelligence and Data Science, Ramco Institute of Technology,Rajapalayam,India-626117
DOI: 10.55041/ijsmt.v2i3.276
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

The exponential growth of AI-generated images produced by Generative Adversarial Networks (GANs) and diffusion-based models has created an urgent need for reliable image quality evaluation systems. Traditional full-reference metrics such as PSNR and SSIM require pristine ground-truth images, which are unavailable for generative outputs. This limitation necessitates robust Non- Reference Image Quality Assessment (NR-IQA) frameworks.


This paper presents VisionIQ Pro, a hybrid multi- tiered visual quality assessment system that integrates handcrafted statistical signal metrics with deep perceptual Natural Scene Statistics (NSS) metrics such as BRISQUE and NIQE. The system constructs a fused Image Quality Vector and uses a Random Forest regression model trained on KonIQ-10k and LIVE datasets to predict Mean Opinion Score (MOS). Additionally, the framework incorporates Explainable AI through Sobel-based sharpness density mapping and actionable feedback logic.


Experimental analysis demonstrates strong correlation (PLCC > 0.85) with human subjective perception. The proposed system serves as a scalable, interpretable, and production-ready solution for automated auditing of AI-generated and real-world images

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 03
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Mar 26 2026
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Kannan, B. M. & Dhanalakshmi, K. (2026). Evaluating Non-Reference Image Quality Metrics for AI-Generated Images. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.276

Kannan, B., and K. Dhanalakshmi. "Evaluating Non-Reference Image Quality Metrics for AI-Generated Images." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.276.

Kannan, B., and K. Dhanalakshmi. "Evaluating Non-Reference Image Quality Metrics for AI-Generated Images." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.276.

References
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Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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