ADVANCEMENTS IN IMAGE QUALITY ASSESSMENT: A COMPREHENSIVE SURVEY

Authors

  • Axmedov Abdulazizxon Ganijon o‘g‘li Namangan State University, Namangan
  • Dadaxanov Musoxon Xoshimxonovich Namangan State University

Keywords:

Image Quality Assessment (IQA), Full-Reference IQA (FR-IQA), No-Reference IQA (NR-IQA), Reduced-Reference IQA (RR-IQA), Deep Learning, Convolutional Neural Networks (CNNs), Semi-Supervised Learning, Low-Dose CT Image Quality, Image Distortion Metrics

Abstract

Image Quality Assessment (IQA) plays a critical role in ensuring the effectiveness of various image-based applications, including medical imaging, autonomous driving, entertainment media, and more. This work offers an in-depth assessment of IQA techniques, emphasizing the improvements in no-reference (NR) techniques generated by deep learning while also highlighting conventional full-reference (FR) and reduced-reference (RR) models. The survey includes a comparison of metrics, datasets, and methods used for both synthetic and real-world images. We discuss the difficulties when evaluating IQA models and offer ideas for future study, with a focus on addressing a variety of distortions, including aspects of human perception and resolving data scarcity problems through semi-supervised learning.

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Published

2024-10-28

How to Cite

Axmedov, A., & Dadaxanov , M. (2024). ADVANCEMENTS IN IMAGE QUALITY ASSESSMENT: A COMPREHENSIVE SURVEY. DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2(5), 34–39. Retrieved from https://dtai.tsue.uz/index.php/dtai/article/view/v2i56