A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
This survey is significant for researchers and developers in the field of image processing and computer vision, providing a comprehensive overview of current image quality assessment methodologies and suggesting future research directions.
This survey tackles the challenge of image quality assessment, analyzing contemporary methodologies and highlighting the need for distortion-specific methods, with a focus on practicality and interpretability. The survey covers various approaches, including conventional statistical measures and cutting-edge deep learning models.
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.