IDEAS home Printed from https://ideas.repec.org/a/epw/ejai00/v4y2025i5id1070.html

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

Author

Listed:
  • Yashaswini Sridhar

    (Cambridge Institute of Technology, KR Puram, Karnataka, India)

  • Jayanthi Muniram Gajendra

    (Cambridge Institute of Technology, KR Puram, Karnataka, India)

  • Preethi Srinivasalu

    (Cambridge Institute of Technology, KR Puram, Karnataka, India)

  • Manjunath Srinivasappa

    (REVA University, Karnataka, India)

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

Handle: RePEc:epw:ejai00:v:4:y:2025:i:5:id:1070
DOI: 10.24018/ejai.2025.4.5.70
as

Download full text from publisher

File URL: https://eu-opensci.org/index.php/ejai/article/view/1070
File Function: Abstract page
Download Restriction: no
File URL: https://eu-opensci.org/index.php/ejai/article/download/1070/439
File Function: Full text
Download Restriction: no
File URL: https://libkey.io/10.24018/ejai.2025.4.5.70?utm_source=ideas
LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
---><---

More about this item

Keywords

;
;
;
;

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:ejai00:v:4:y:2025:i:5:id:1070. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Support Team (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejai .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.