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Evaluating Non-Reference Image Quality Metrics for AI-Generated Images: A Novel Approach

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  • Yashaswini Sridhar
  • Jayanthi Muniram Gajendra
  • Preethi Srinivasalu
  • Manjunath Srinivasappa

Abstract

This research paper explores the development and evaluation of non-reference image quality metrics specifically tailored for AI-generated images created by task-specific prompts. Given the unique challenges posed by such images, traditional metrics often fall short in assessing their perceptual quality and alignment with the provided prompts. This study introduces a novel approach that integrates multi-granularity similarity measurements and task-specific prompts to evaluate both perceptual and alignment quality. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed metrics, offering a new standard for assessing AI-generated images.

Suggested Citation

  • Yashaswini Sridhar & Jayanthi Muniram Gajendra & Preethi Srinivasalu & Manjunath Srinivasappa, 2025. "Evaluating Non-Reference Image Quality Metrics for AI-Generated Images: A Novel Approach," European Journal of Artificial Intelligence and Machine Learning, European Open Science, vol. 4(5), pages 18-25, October.
    • Handle: RePEc:epw:ejai00:v:4:y:2025:i:5:id:1070
    DOI: 10.24018/ejai.2025.4.5.70
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