A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
Journal:
arXiv
Published Date:
Feb 12, 2025
Abstract
Image quality assessment (IQA) represents a pivotal challenge in
image-focused technologies, significantly influencing the advancement
trajectory of image processing and computer vision. Recently, IQA has witnessed
a notable surge in innovative research efforts, driven by the emergence of
novel architectural paradigms and sophisticated computational techniques. This
survey delivers an extensive analysis of contemporary IQA methodologies,
organized according to their application scenarios, serving as a beneficial
reference for both beginners and experienced researchers. We analyze the
advantages and limitations of current approaches and suggest potential future
research pathways. The survey encompasses both general and specific IQA
methodologies, including conventional statistical measures, machine learning
techniques, and cutting-edge deep learning models such as convolutional neural
networks (CNNs) and Transformer models. The analysis within this survey
highlights the necessity for distortion-specific IQA methods tailored to
various application scenarios, emphasizing the significance of practicality,
interpretability, and ease of implementation in future developments.
