A review of artificial intelligence-generated image quality assessment: from evaluation dimensions to model methods

doi:10.11996/JG.j.2095-302X.2026050941

Abstract

Abstract:

Image Quality Assessment (IQA) is a valuable and vital research topic in image processing and computer vision, aiming to construct image quality assessment models in accordance with human visual perception. With the rapid advancement of Artificial Intelligence-Generated Content (AIGC), remarkable progress has been made in visual generation, particularly in image synthesis. However, due to the inherent limitations of image generation mechanisms, Artificial Intelligence-Generated Images (AIGI) often exhibit various quality defects, including semantic drift, unreasonable structures, and visual artifacts. Traditional IQA methods fail to adequately capture these unique defects, thus falling short in meeting the evaluation needs for AIGI. Consequently, AIGI Quality Assessment (AIGIQA) has emerged as a prominent research topic in computer vision. Existing technical frameworks and research methodologies of AIGIQA were comprehensively reviewed, systematically investigated, and thoroughly analyzed. First, three representative image generation paradigms, namely generative adversarial networks, autoregressive models, and diffusion models, were reviewed, and the typical image quality defects associated with different generation mechanisms were analyzed. Next, the quality evaluation dimensions of AIGI were summarized from five perspectives, including technical issues, AI artifacts, unnaturalness, discrepancy, and aesthetics. Furthermore, commonly used performance evaluation metrics for AIGIQA models were reviewed, and publicly available datasets were introduced and compared. In addition, the development of traditional IQA methods was revisited, and their limitations in assessing AIGI were thoroughly discussed. Existing AIGIQA approaches were further categorized, with particular emphasis on the technical frameworks, core ideas, and limitations of representative methods based on cross-modal alignment and Multimodal Large Language Models (MLLMs). Finally, the opportunities and challenges faced by AIGIQA in quality representation, dataset support, model construction, and interpretability were discussed, and potential directions for future research were identified, providing valuable references for subsequent studies in this field.

Key words: artificial intelligence-generated images, image quality assessment, evaluation dimensions, evaluation metrics, assessment methods

CLC Number:

YANG Liran, LI Kexin, ZHANG Shiyin, SU Pan. A review of artificial intelligence-generated image quality assessment: from evaluation dimensions to model methods[J]. Journal of Graphics, 2026, 47(5): 941-953.

Figures/Tables 9

References 102

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评估维度	数据集标注	说明
技术问题(模糊、噪声)	感知质量/技术质量	表征图像低层视觉信号的退化，主要体现在图像清晰度与细节表达，与感知质量和技术质量评分相关
AI伪影(重复纹理、畸变)	感知质量/真实性/合理性/自然性	由生成机制产生的结构异常与重复模式，同时体现在视觉质量与自然属性相关评价维度
不自然性(光照/结构异常)	真实性/合理性/自然性/感知质量	反映图像对真实世界物理规律与结构关系的偏离，主要体现在真实性与自然性评价中
差异性(语义偏移)	图文一致性	表征图像内容与文本语义之间的不一致性，与图文一致性评价结果相关
美学(构图、风格)	感知质量	体现图像在构图与视觉表现层面的高层感知属性，综合反映在整体感知质量评分中

评估维度	数据集标注	说明
技术问题(模糊、噪声)	感知质量/技术质量	表征图像低层视觉信号的退化，主要体现在图像清晰度与细节表达，与感知质量和技术质量评分相关
AI伪影(重复纹理、畸变)	感知质量/真实性/合理性/自然性	由生成机制产生的结构异常与重复模式，同时体现在视觉质量与自然属性相关评价维度
不自然性(光照/结构异常)	真实性/合理性/自然性/感知质量	反映图像对真实世界物理规律与结构关系的偏离，主要体现在真实性与自然性评价中
差异性(语义偏移)	图文一致性	表征图像内容与文本语义之间的不一致性，与图文一致性评价结果相关
美学(构图、风格)	感知质量	体现图像在构图与视觉表现层面的高层感知属性，综合反映在整体感知质量评分中

数据集	图像数量	生成模型数量	评分类型	评分区间	评分标注维度	说明
AGIQA-1K^[28]	1 080	2	MOS	[0,5]	质量	最早提出，规模较小且标注有限
AGIQA-3K^[1]	2 982	6	MOS	[0,5]	质量、图文对齐	引入多类生成模型，早期生成模型性能不及前沿水平
AIGCIQA2023^[29]	2 400	6	MOS	[0,100]	质量、真实性、图文对齐	提供三维主观评分，数据来源与生成条件相对集中
Pick-A-Pic^[33]	500 000	3	成对偏好(Pairwise preference)	—	人类主观偏好	大规模人类偏好数据集，难以直接用于回归任务
AIGIQA-20K^[34]	20 000	15	MOS	[0,5]	质量和图文对齐综合考量	大规模的细粒度数据库，多维因素被压缩为单一分数
PKU-AIGIQA-4K^[35]	4 000	3	MOS	[0,5]	质量、真实性、图文对齐	支持图像到图像和文本到图像场景，生成模型覆盖范围窄
AGIN^[30]	6 049	18	MOS	[0,5]	技术质量、合理性、自然性	第一个研究图像自然性问题的数据集，侧重特定任务场景

数据集	图像数量	生成模型数量	评分类型	评分区间	评分标注维度	说明
AGIQA-1K^[28]	1 080	2	MOS	[0,5]	质量	最早提出，规模较小且标注有限
AGIQA-3K^[1]	2 982	6	MOS	[0,5]	质量、图文对齐	引入多类生成模型，早期生成模型性能不及前沿水平
AIGCIQA2023^[29]	2 400	6	MOS	[0,100]	质量、真实性、图文对齐	提供三维主观评分，数据来源与生成条件相对集中
Pick-A-Pic^[33]	500 000	3	成对偏好(Pairwise preference)	—	人类主观偏好	大规模人类偏好数据集，难以直接用于回归任务
AIGIQA-20K^[34]	20 000	15	MOS	[0,5]	质量和图文对齐综合考量	大规模的细粒度数据库，多维因素被压缩为单一分数
PKU-AIGIQA-4K^[35]	4 000	3	MOS	[0,5]	质量、真实性、图文对齐	支持图像到图像和文本到图像场景，生成模型覆盖范围窄
AGIN^[30]	6 049	18	MOS	[0,5]	技术质量、合理性、自然性	第一个研究图像自然性问题的数据集，侧重特定任务场景

类型	方法	AGIQA-3K
类型	方法	SRCC	KRCC	PLCC
基于手工制作	DSIQA^[72](2009)	0.495 5	0.340 3	0.548 8
	NIQE^[60](2012)	0.562 3	0.387 6	0.517 1
	SISBLIM^[73](2014)	0.547 9	0.378 8	0.647 7
	CEIQ^[74](2019)	0.322 8	0.222 0	0.416 6
基于分布距离	ICS^[75](2016)	0.093 1	0.062 6	0.096 4
	FID^[76](2017)	0.173 3	0.115 8	0.186 0
	KID^[77](2018)	0.102 3	0.069 2	0.078 6
基于SVR	GMLF^[78](2014)	0.698 7	0.511 9	0.818 1
基于SVR	BMPRI^[79](2018)	0.679 4	0.497 6	0.791 2
基于深度学习方法	CNNIQA^[61](2014)	0.747 8	0.558 0	0.846 9
	DBCNN^[63](2018)	0.820 7	0.633 6	0.875 9
	HyperNet^[64](2020)	0.835 5	0.648 8	0.890 3
	CLIPIQA^[80](2022)	0.842 6	0.646 8	0.805 3