Image quality assessment (IQA) aims to measure the consistency between an objective algorithm output and a subjective perception measurement. This article focuses on this complex relationship in the context of color image scenarios—color image quality assessment (CIQA). This review systematically investigates CIQA applications in image compression, processing optimization, and domain-specific scenarios, analyzes benchmark datasets and assessment metrics, and categorizes CIQA algorithms into full-reference (FR), reduced-reference (RR) and no-reference (NR) methods. In this study, color images are evaluated using a newly developed CIQA framework. Focusing on FR and NR methods, FR methods leverage reference images with machine learning, visual perception models, and mathematical frameworks, while NR methods utilize distortion-only features through feature fusion and extraction techniques. Specialized CIQA algorithms are developed for robotics, low-light, and underwater imaging. Despite progress, challenges remain in cross-domain adaptability, generalization, and contextualized assessment. Future directions may include prototype-based cross-domain adaptation, fidelity–structure balancing, spatiotemporal consistency integration, and CIQA–restoration synergy to meet emerging demands.

Assessing the quality of Artificial Intelligence-Generated Content (AIGC) images remains a critical challenge, as conventional Image Quality Assessment (IQA) methods often fail to capture the semantic consistency between generated images and their textual prompts. This study aims to establish an interpretable and efficient multimodal framework for evaluating AIGC image quality. The research addresses three key scientific questions: how to leverage structured prompt semantics for more interpretable assessments, how to enable parameter-efficient yet accurate adaptation, and how to achieve unified handling of perceptual and semantic subtasks. To this end, we propose the Prompt-Enhanced Low-Rank Adaptation (PELA) framework, which integrates Hierarchical Prompt Engineering and Low-Rank Adaptation within a CLIP-based backbone. Hierarchical prompts encode multi-level semantics for fine-grained evaluation, while low-rank adaptation enables lightweight, task-specific optimization. Experiments conducted on AGIQA-1K, AGIQA-3K, and AIGCIQA-2023 datasets demonstrate that PELA achieves superior correlation with human perceptual judgments and sets new state-of-the-art results across multiple metrics. The findings confirm that combining structured prompt semantics with efficient adaptation offers a compact, interpretable, and scalable paradigm for multimodal image quality assessment.