A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook

• URL: http://arxiv.org/abs/2502.08540v1
• Date: Wed, 12 Feb 2025 16:24:22 GMT
• Title: A Survey on Image Quality Assessment: Insights, Analysis, and Future Outlook
• Authors: Chengqian Ma, Zhengyi Shi, Zhiqiang Lu, Shenghao Xie, Fei Chao, Yao Sui,
• Abstract summary: Image quality assessment (IQA) represents a pivotal challenge in image-focused technologies. IQA has witnessed a notable surge in innovative research efforts, driven by the emergence of novel architectural paradigms. This survey delivers an extensive analysis of contemporary IQA methodologies, organized according to their application scenarios.
• Score: 6.925820483833189
• License:
• 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.

Related papers

• A Survey of Model Architectures in Information Retrieval [64.75808744228067]
  We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation. We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs) We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• AI-generated Image Quality Assessment in Visual Communication [72.11144790293086]
  AIGI-VC is a quality assessment database for AI-generated images in visual communication. The dataset consists of 2,500 images spanning 14 advertisement topics and 8 emotion types. It provides coarse-grained human preference annotations and fine-grained preference descriptions, benchmarking the abilities of IQA methods in preference prediction, interpretation, and reasoning.
  arXiv  Detail & Related papers  (2024-12-20T08:47:07Z)
• A Comprehensive Survey of Action Quality Assessment: Method and Benchmark [25.694556140797832]
  Action Quality Assessment (AQA) quantitatively evaluates the quality of human actions, providing automated assessments that reduce biases in human judgment. Recent advances in AQA have introduced innovative methodologies, but similar methods often intertwine across different domains. The lack of a unified benchmark and limited computational comparisons hinder consistent evaluation and fair assessment of AQA approaches.
  arXiv  Detail & Related papers  (2024-12-15T10:47:26Z)
• Socio-Economic Consequences of Generative AI: A Review of Methodological Approaches [0.0]
  We identify the primary methodologies that may be used to help predict the economic and social impacts of generative AI adoption. Through a comprehensive literature review, we uncover a range of methodologies poised to assess the multifaceted impacts of this technological revolution.
  arXiv  Detail & Related papers  (2024-11-14T09:40:25Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions [6.2719115566879236]
  Diffusion Models (DMs) have emerged as a powerful tool for image data augmentation. DMs generate realistic and diverse images by learning the underlying data distribution. Current challenges and future research directions in the field are discussed.
  arXiv  Detail & Related papers  (2024-07-04T18:06:48Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• Image Quality Assessment in the Modern Age [53.19271326110551]
  This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA) We will first revisit several subjective quality assessment methodologies, with emphasis on how to properly select visual stimuli. Both hand-engineered and (deep) learning-based methods will be covered.
  arXiv  Detail & Related papers  (2021-10-19T02:38:46Z)
• Quantifying Visual Image Quality: A Bayesian View [31.494753153095587]
  Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their perceptual quality by human observers. IQA modeling plays a special bridging role between vision science and engineering practice.
  arXiv  Detail & Related papers  (2021-01-30T09:34:23Z)
• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [76.20963684020145]
  Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. Bizarre approximation and ensemble learning techniques are two most widely-used UQ methods in the literature. This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning.
  arXiv  Detail & Related papers  (2020-11-12T06:41:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.