Blind Image Quality Assessment (BIQA) is essential for automatically
evaluating the perceptual quality of visual signals without access to the
references. In this survey, we provide a comprehensive analysis and discussion
of recent developments in the field of BIQA. We have covered various aspects,
including hand-crafted BIQAs that focus on distortion-specific and
general-purpose methods, as well as deep-learned BIQAs that employ supervised
and unsupervised learning techniques. Additionally, we have explored multimodal
quality assessment methods that consider interactions between visual and audio
modalities, as well as visual and text modalities. Finally, we have offered
insights into representative BIQA databases, including both synthetic and
authentic distortions. We believe this survey provides valuable understandings
into the latest developments and emerging trends for the visual quality
community.

本综述对盲图像质量评估 (BIQA) 领域的最新发展和新兴趋势进行了全面分析和讨论，包括专注于特定畸变和通用方法的手工制作 BIQA、采用监督和无监督学习技术的深度学习 BIQA，以及考虑视觉与音频、视觉与文本模态之间相互作用的多模态质量评估方法。同时，还探讨了代表性的 BIQA 数据库，包括合成和真实畸变。我们相信，本综述对视觉质量社区的最新进展和新兴趋势提供了有价值的理解。