No-Reference Image Quality Assessment: Past, Present, and Future

Abstract

No-reference image quality assessment (NR-IQA) has garnered significant attention due to its critical role in various image processing applications. This survey provides a comprehensive and systematic review of NR-IQA methods, datasets, and challenges, offering new perspectives and insights for the field. Specifically, we propose a novel taxonomy for NR-IQA methods based on distortion scenarios and design principles, which distinguishes this work from previous surveys. Representative methods within each category are thoroughly examined, with a focus on their strengths, limitations, and performance characteristics. Additionally, we review 20 widely used NR-IQA datasets that serve as benchmarks for evaluating these methods, providing detailed information on the number of images, distortion types, and distortion levels for each dataset. Furthermore, we identify and discuss key challenges currently faced by NR-IQA methods, such as handling diverse and complex distortions, ensuring generalisation across datasets and devices, and achieving real-time performance. We also suggest potential future research directions to address these issues. In summary, this survey offers a comprehensive and systematic examination of NR-IQA methods, datasets, and challenges, offering valuable insights and guidance for researchers and practitioners working in the NR-IQA domain.