Blind Image Quality Assessment: A Brief Survey

TL;DR

This survey reviews recent advances in Blind Image Quality Assessment, covering hand-crafted and deep learning methods, multimodal approaches, and key databases, highlighting emerging trends and challenges in the field.

Contribution

It provides a comprehensive overview of BIQA methods, datasets, and recent developments, offering valuable insights into current trends and future directions.

Findings

Deep learning methods outperform traditional approaches.

Multimodal BIQA incorporates audio and text modalities.

Authentic distortion datasets are crucial for real-world applications.

Abstract

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.