A Systematic Review of Image Quality Assessment Methods: Hitherto and Futuristic Perspectives

Digital images are the most frequently generated and communicated media from a diverse range of applications, such as medical imaging, satellite imagery, social media, secured surveillance, robotics, computer vision, etc. The image quality is one of the vital characteristics that is essential for computer vision tasks, where key features play a significant role in object detection and classification. Usually, images are subjected to several inconsistencies, such as noise, blurring, resolution, intensity variations, distortions, etc. Thus, image quality assessment (IQA) and enhancement are imperative for accurate and efficient machine learning models in computer vision applications. This study aims to provide an in-depth review and analysis of existing state-of-the-art (SOTA) IQA techniques. Besides, a new taxonomy is presented to classify various IQA techniques, as well as various evaluation protocols (i.e. metrics and benchmark datasets) that are frequently used to assess these methods. However, noteworthy progress has been observed in IQA techniques, but there exist several research challenges in this active field of digital imaging. One of the crucial challenges is to deploy modern deep learning models for IQA and enhancement, as these require comparatively larger datasets. The quality assessment of generative AI-based images is an additional challenge, as they are subjected to several quality issues. Moreover, multi-spectral and super-resolution image quality assessment is another open challenge that requires the attention of investigators in this field of research.