AIGC图像质量评估指标研究
作者:
中图分类号:

TP18

基金项目:

河南省高等教育教学改革研究与实践项目(2024SJGLX173,2019SJGLX690);河南省重点研发专项(231111210200,241111210300);中央引导地方科技发展专项(Z2022-1343001);黄河水利职业技术学院测绘地理信息职业教育研究课题(2021CHYB01)


AIGC image quality evaluation indicators
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • | |
  • 文章评论
    摘要:

    人工智能生成内容(AIGC)技术可为人类提供各种类型的信息生成服务,如何对AIGC进行准确的质量评估,是当前亟待解决的问题.本文主要针对大模型生成图像的质量及其评估指标开展深入研究.首先,从技术方面概述了当前评估AIGC的常见方法,如深度学习方法和计算机视觉方法等,介绍并分析了准确性、相关性、一致性、可解释性等指标在不同类型生成内容评估方面的表现.然后,为了展示评估指标的实际作用,以百度文心一言为例,对其生成的图像进行评估实验:使用直方图和噪点数量等量化指标对生成图像进行客观评估;使用整体协调性和美观性等视觉感官指标对生成图像进行主观评估.最后,综合对比客观评估和主观评估的结果,筛选出色偏、噪点数量、心理预期等AIGC产品质量评估的高可靠性指标.实验结果验证了综合使用主客观评估指标进行AIGC产品评估方法的有效性和可靠性.

    Abstract:

    Artificial Intelligence Generated Content (AIGC) technology offers a wide range of information generation services.However,the accurate assessment of AIGC quality is a critical issue that needs to be addressed.This study delves into the quality of images generated by large models and their evaluation metrics.First,it summarizes common methods for evaluating AIGC from a technical perspective,such as deep learning and computer vision approaches.The study introduces the metrics used in these evaluation methods,including accuracy,relevance,consistency,and interpretability,and examines their performance in evaluating diverse generated content.Then,to demonstrate the practical application of these evaluation metrics,this study conducts an evaluation experiment using images generated by ERNIE Bot as an example.Objective evaluation of the generated images is carried out through quantitative metrics like histograms and noise counts,while subjective evaluation focuses on the overall coordination and aesthetic appeal of the images.Finally,by comparing the results of objective and subjective evaluations,this study identifies highly reliable metrics for evaluating the quality of AIGC images,including color bias,noise count,and psychological expectations.This research provides a theoretical foundation for evaluating the AIGC quality and verifies the effectiveness and reliability of a combined approach using both objective and subjective metrics for AIGC product evaluation through experimental results.

    参考文献
    相似文献
    引证文献
引用本文

邢润媚,常升龙,何宽,朱曙光,高琼,胡昊. AIGC图像质量评估指标研究[J].南京信息工程大学学报(自然科学版),2025,17(1):63-73
XING Runmei, CHANG Shenglong, HE Kuan, ZHU Shuguang, GAO Qiong, HU Hao. AIGC image quality evaluation indicators[J]. Journal of Nanjing University of Information Science & Technology, 2025,17(1):63-73

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2024-05-15
  • 在线发布日期: 2025-02-22

地址:江苏省南京市宁六路219号    邮编:210044

联系电话:025-58731025    E-mail:nxdxb@nuist.edu.cn

南京信息工程大学学报 ® 2025 版权所有  技术支持:北京勤云科技发展有限公司