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Blind Image Quality Assessment: A Brief Survey (2312.16551v1)

Published 27 Dec 2023 in cs.CV and cs.MM

Abstract: Blind Image Quality Assessment (BIQA) is essential for automatically evaluating the perceptual quality of visual signals without access to the references. In this survey, we provide a comprehensive analysis and discussion of recent developments in the field of BIQA. We have covered various aspects, including hand-crafted BIQAs that focus on distortion-specific and general-purpose methods, as well as deep-learned BIQAs that employ supervised and unsupervised learning techniques. Additionally, we have explored multimodal quality assessment methods that consider interactions between visual and audio modalities, as well as visual and text modalities. Finally, we have offered insights into representative BIQA databases, including both synthetic and authentic distortions. We believe this survey provides valuable understandings into the latest developments and emerging trends for the visual quality community.

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References (136)
  1. Towards instrumental quality assessment of multisensory immersive experiences using a biosensor-equipped head-mounted display. Springer Quality and User Experience, 8(1):9, 2023.
  2. A comprehensive performance evaluation of image quality assessment algorithms. IEEE Access, 7:140030–140070, 2019.
  3. Blind quality assessment of screen content images via macro-micro modeling of tensor domain dictionary. IEEE Transactions on Multimedia, 23(1):4259–4271, 2021.
  4. Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2):423–443, 2018.
  5. Deep neural networks for no-reference and full-reference image quality assessment. IEEE Transactions on Image Processing, 27(1):206–219, 2018.
  6. Blind image quality assessment by simulating the visual cortex. Springer The Visual Computer, 39(10):4639–4656, 2023.
  7. Deep Neural Networks For Full-Reference And No-Reference Audio-Visual Quality Assessment. In IEEE International Conference on Image Processing (ICIP), pages 1429–1433, 2021.
  8. Subjective and objective audio-visual quality assessment for user generated content. IEEE Transactions on Image Processing, 32:3847–3861, 2023.
  9. No-reference screen content image quality assessment with unsupervised domain adaptation. IEEE Transactions on Image Processing, 30(1):5463–5476, 2021.
  10. Study of 3D virtual reality picture quality. IEEE Journal of Selected Topics in Signal Processing, 14(1):89–102, 2020.
  11. Unsupervised curriculum domain adaptation for no-reference video quality assessment. In IEEE International Conference on Computer Vision (ICCV), pages 5178–5187, 2021.
  12. Image aesthetic assessment: An experimental survey. IEEE Signal Processing Magazine, 34(4):80–106, 2017.
  13. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2567–2581, 2022.
  14. Reduced reference image and video quality assessments: review of methods. EURASIP Journal on Image and Video Processing, 2022(1):1–31, 2022.
  15. Perceptual Quality Assessment of Omnidirectional Images. In IEEE International Symposium on Circuits and Systems (ISCAS), pages 1–5, 2018.
  16. Thermal Infrared Single Image Dehazing and Blind Image Quality Assessment. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 459–469, 2023.
  17. Perceptual quality assessment for screen content images by spatial continuity. IEEE Transactions on Circuits and Systems for Video Technology, 30(11):4050–4063, 2020.
  18. Perceptual quality assessment of omnidirectional images. In AAAI Conference on Artificial Intelligence (AAAI), pages 580–588, 2022.
  19. No reference quality assessment for screen content images with both local and global feature representation. IEEE Transactions on Image Processing, 27(4):1600–1610, 2017.
  20. No-reference image quality assessment using orthogonal color planes patterns. IEEE Transactions on Multimedia, 20(12):3353–3360, 2018.
  21. No-reference image quality assessment with reinforcement recursive list-wise ranking. In AAAI Conference on Artificial Intelligence (AAAI), pages 8336–8343, 2019.
  22. No-reference quality assessment of screen content pictures. IEEE Transactions on Image Processing, 26(8):4005–4018, 2017.
  23. PEID: A perceptually encrypted image database for visual security evaluation. IEEE Transactions on Information Forensics and Security, 15(1):1151–1163, 2019.
  24. David S Hands. A basic multimedia quality model. IEEE Transactions on Multimedia, 6(6):806–816, 2004.
  25. KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE Transactions on Image Processing, 29(1):4041–4056, 2020.
  26. UID2021: an underwater image dataset for evaluation of no-reference quality assessment metrics. ACM Transactions on Multimedia Computing, Communications and Applications, 19(4):1–24, 2023.
  27. Modeling the perceptual quality of immersive images rendered on head mounted displays: Resolution and compression. IEEE Transactions on Image Processing, 27(12):6039–6050, 2018.
  28. Cubemap-based perception-driven blind quality assessment for 360-degree images. IEEE Transactions on Image Processing, 30(1):2364–2377, 2021.
  29. Blind Quality Evaluator of Tone-Mapped HDR and Multi-Exposure Fused Images for Electronic Display. IEEE Transactions on Consumer Electronics, 67(4):350–362, 2021.
  30. Convolutional neural networks for no-reference image quality assessment. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1733–1740, 2014.
  31. MUSIQ: Multi-scale image quality transformer. In IEEE International Conference on Computer Vision (ICCV), pages 5148–5157, 2021.
  32. Fully deep blind image quality predictor. IEEE Journal of Selected Topics in Signal Processing, 11(1):206–220, 2017.
  33. Deep CNN-based blind image quality predictor. IEEE Transactions on Neural Networks and Learning Systems, 30(1):11–24, 2019.
  34. Subjective quality assessment database of HDR images compressed with JPEG XT. In IEEE International Workshop on Quality of Multimedia Experience (QoMEX), pages 1–6, 2015.
  35. Large-scale crowdsourced study for tone-mapped HDR pictures. IEEE Transactions on Image Processing, 26(10):4725–4740, 2017.
  36. No-reference quality assessment of tone-mapped HDR pictures. IEEE Transactions on Image Processing, 26(6):2957–2971, 2017.
  37. Categorical subjective image quality CSIQ database, 2009.
  38. Norm-in-norm loss with faster convergence and better performance for image quality assessment. In ACM International Conference on Multimedia (MM), pages 789–797, 2020.
  39. BSD: Blind image quality assessment based on structural degradation. Elsevier Neurocomputing, 236(1):93–103, 2017.
  40. Blind image quality assessment using statistical structural and luminance features. IEEE Transactions on Multimedia, 18(12):2457–2469, 2016.
  41. KADID-10k: A large-scale artificially distorted IQA database. In International Conference on Quality of Multimedia Experience (QoMEX), pages 1–3, 2019.
  42. Hallucinated-IQA: No-reference image quality assessment via adversarial learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 732–741, 2018.
  43. A Multiscale Approach to Deep Blind Image Quality Assessment. IEEE Transactions on Image Processing, 32:1656–1667, 2023.
  44. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9):1–35, 2023.
  45. No-reference image quality assessment by wide-perceptual-domain scorer ensemble method. IEEE Transactions on Image Processing, 27(3):1138–1151, 2018.
  46. RankIQA: Learning from rankings for no-reference image quality assessment. In IEEE International Conference on Computer Vision (ICCV), pages 1040–1049, 2017.
  47. Exploiting unlabeled data in CNNs by self-supervised learning to rank. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8):1862–1878, 2019.
  48. Blind image quality assessment by natural scene statistics and perceptual characteristics. ACM Transactions on Multimedia Computing, Communications, and Applications, 16(3):1–91, 2020.
  49. Waterloo exploration database: New challenges for image quality assessment models. IEEE Transactions on Image Processing, 26(2):1004–1016, 2016.
  50. dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs. IEEE Transactions on Image Processing, 26(8):3951–3964, 2017.
  51. End-to-end blind image quality assessment using deep neural networks. IEEE Transactions on Image Processing, 27(3):1202–1213, 2018.
  52. Remember and Reuse: Cross-Task Blind Image Quality Assessment via Relevance-aware Incremental Learning. In ACM International Conference on Multimedia (MM), pages 5248–5256, 2021.
  53. Image quality assessment using contrastive learning. IEEE Transactions on Image Processing, 31(1):4149–4161, 2022.
  54. Subjective and objective quality assessment of stitched images for virtual reality. IEEE Transactions on Image Processing, 28(11):5620–5635, 2019.
  55. A Multi-Attribute Blind Quality Evaluator for Tone-Mapped Images. IEEE Transactions on Multimedia, 22(8):1939–1954, 2020.
  56. Non-distortion-specific no-reference image quality assessment: A survey. Elsevier Information Sciences, 301:141–160, 2015.
  57. Helard A Becerra Martinez and Mylene CQ Farias. Combining audio and video metrics to assess audio-visual quality. Springer Multimedia Tools and Applications, 77(18):23993–24012, 2018.
  58. Hearing lips and seeing voices. Nature, 264(5588):746–748, 1976.
  59. Screen content quality assessment: Overview, benchmark, and beyond. ACM Computing Surveys, 54(9):1–36, 2021.
  60. Unified blind quality assessment of compressed natural, graphic, and screen content images. IEEE Transactions on Image Processing, 26(11):5462–5474, 2017.
  61. Study of subjective and objective quality assessment of audio-visual signals. IEEE Transactions on Image Processing, 29(5588):6054–6068, 2020.
  62. A multimodal saliency model for videos with high audio-visual correspondence. IEEE Transactions on Image Processing, 29:3805–3819, 2020.
  63. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695–4708, 2012.
  64. Making a ‘Completely Blind’ Image Quality Analyzer. IEEE Signal Processing Letters, 20(3):209–212, 2013.
  65. A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters, 17(5):513–516, 2010.
  66. Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20(12):3350–3364, 2011.
  67. ESIM: Edge similarity for screen content image quality assessment. IEEE Transactions on Image Processing, 26(10):4818–4831, 2017.
  68. Sdd-fiqa: Unsupervised face image quality assessment with similarity distribution distance. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7670–7679, 2021.
  69. The influence of subjects and environment on audiovisual subjective tests: An international study. IEEE Journal of Selected Topics in Signal Processing, 6(6):640–651, 2012.
  70. Image database TID2013: Peculiarities, results and perspectives. Signal processing: Image communication, 30:57–77, 2015.
  71. TID2008 - A database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics, 10(4):30–45, 2009.
  72. Multimedia quality assessment [DSP Forum]. IEEE Signal Processing Magazine, 28(6):164–177, 2011.
  73. Learning transferable visual models from natural language supervision. In PMLR International Conference on Machine Learning (ICML), pages 8748–8763, 2021.
  74. RAN4IQA: Restorative adversarial nets for no-reference image quality assessment. In AAAI Conference on Artificial Intelligence (AAAI), pages 7308–7314, 2018.
  75. Impacts of viewing conditions on hdr-vdp2. In IEEE European Signal Processing Conference (EUSIPCO), pages 1442–1446, 2018.
  76. Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21(8):3339–3352, 2012.
  77. Image quality assessment for machine learning tasks using meta-reinforcement learning. Elsevier Medical Image Analysis, 78(1):102427, 2022.
  78. Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5846–5855, 2023.
  79. Image information and visual quality. IEEE Transactions on image processing, 15(2):430–444, 2006.
  80. Objective Object Segmentation Visual Quality Evaluation: Quality Measure and Pooling Method. ACM Transactions on Multimedia Computing, Communications, and Applications, 18(3):1–19, 2022.
  81. Study on subjective quality assessment of screen content images. In IEEE Picture Coding Symposium (PCS), pages 75–79, 2015.
  82. Harmonized multimodal learning with Gaussian process latent variable models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3):858–872, 2019.
  83. A brief survey on no-reference image quality assessment methods for magnetic resonance images. MDPI Journal of Imaging, 8(6):160, 2022.
  84. Interactions in perceived quality of auditory-visual displays. MIT Press Presence: Teleoperators & Virtual Environments, 9(6):557–580, 2000.
  85. Subjective and objective quality assessment of transparently encrypted JPEG2000 images. In ACM workshop on Multimedia and Security, pages 247–252, 2010.
  86. Blindly assess image quality in the wild guided by a self-adaptive hyper network. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3667–3676, 2020.
  87. Perceptual quality assessment of omnidirectional images as moving camera videos. IEEE Transactions on Visualization and Computer Graphics, 28(8):3022–3034, 2022.
  88. A large-scale compressed 360-degree spherical image database: From subjective quality evaluation to objective model comparison. In IEEE International Workshop on Multimedia Signal Processing (MMSP), pages 1–6, 2018.
  89. Blind quality assessment for in-the-wild images via hierarchical feature fusion and iterative mixed database training. IEEE Journal of Selected Topics in Signal Processing, pages 1–15, 2023.
  90. MC360IQA: A multi-channel CNN for blind 360-degree image quality assessment. IEEE Journal of Selected Topics in Signal Processing, 14(1):64–77, 2020.
  91. A new framework for quality assessment of high-resolution fingerprint images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(10):1905–1917, 2016.
  92. A light field image quality assessment model based on symmetry and depth features. IEEE Transactions on Circuits and Systems for Video Technology, 31(5):2046–2050, 2021.
  93. Testbed for subjective evaluation of omnidirectional visual content. In IEEE Picture Coding Symposium (PCS), pages 1–5, 2016.
  94. Show and tell: Lessons learned from the 2015 mscoco image captioning challenge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4):652–663, 2016.
  95. Improving the quality of multimedia experience through sensory effects. In IEEE International Workshop on Quality of Multimedia Experience (QoMEX), pages 124–129, 2010.
  96. Low-light Images In-the-wild: A Novel Visibility Perception-guided Blind Quality Indicator. IEEE Transactions on Industrial Informatics, 1(1):1–11, 2022.
  97. Blind Quality Assessment of Night-Time Images Via Weak Illumination Analysis. In IEEE International Conference on Multimedia and Expo (ICME), pages 1–6, 2021.
  98. S-CCR: Super-Complete Comparative Representation for Low-Light Image Quality Inference In-the-wild. In ACM International Conference on Multimedia (MM), pages 5219–5227, 2022.
  99. Blind Multimodal Quality Assessment of Low-light Images. arXiv preprint arXiv:2303.10369, pages 1–15, 2023.
  100. Subjective and objective quality assessment of compressed screen content images. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 6(4):532–543, 2016.
  101. Combining Retargeting Quality and Depth Perception Measures for Quality Evaluation of Retargeted Stereopairs. IEEE Transactions on Multimedia, 24(1):2422–2434, 2022.
  102. Active fine-tuning from gMAD examples improves blind image quality assessment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(1):1–14, 2021.
  103. Contrastive distortion-level learning-based no-reference image-quality assessment. Wiley International Journal of Intelligent Systems, 37(11):8730–8746, 2022.
  104. Visual quality assessment for perceptually encrypted light field images. IEEE Transactions on Circuits and Systems for Video Technology, 31(7):2522–2534, 2020.
  105. Perceived audiovisual quality of low-bitrate multimedia content. IEEE transactions on multimedia, 8(5):973–980, 2006.
  106. End-to-end blind image quality prediction with cascaded deep neural network. IEEE Transactions on Image Processing, 29(1):7414–7426, 2020.
  107. Subjective and objective de-raining quality assessment towards authentic rain image. IEEE Transactions on Circuits and Systems for Video Technology, 30(11):3883–3897, 2020.
  108. Blind night-time image quality assessment: Subjective and objective approaches. IEEE Transactions on Multimedia, 22(5):1259–1272, 2019.
  109. pmbqa: Projection-based blind point cloud quality assessment via multimodal learning. In ACM International Conference on Multimedia (ACM MM), pages 3250–3258, 2023.
  110. Blind image quality assessment based on high order statistics aggregation. IEEE Transactions on Image Processing, 25(9):4444–4457, 2016.
  111. Viewport-based CNN: A multi-task approach for assessing 360 video quality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):2198–2215, 2022.
  112. No-reference/blind image quality assessment: A survey. Taylor & Francis IETE Technical Review, 34(3):223–245, 2017.
  113. Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Transactions on Image Processing, 23(11):4850–4862, 2014.
  114. Naturalness-aware deep no-reference image quality assessment. IEEE Transactions on Multimedia, 21(10):2603–2615, 2019.
  115. Perceptual quality assessment of screen content images. IEEE Transactions on Image Processing, 24(11):4408–4421, 2015.
  116. Fine-Grained Image Quality Caption With Hierarchical Semantics Degradation. IEEE Transactions on Image Processing, 31(1):3578–3590, 2022.
  117. A survey of DNN methods for blind image quality assessment. IEEE Access, 7:123788–123806, 2019.
  118. Objective quality assessment of tone-mapped images. IEEE Transactions on Image processing, 22(2):657–667, 2012.
  119. Telepresence video quality assessment. In Springer European Conference on Computer Vision (ECCV), pages 327–347, 2022.
  120. From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3575–3585, 2020.
  121. A hybrid indicator for realistic blurred image quality assessment. Elsevier Journal of Visual Communication and Image Representation, 94:103848, 2023.
  122. No-reference quality evaluator of transparently encrypted images. IEEE Transactions on Multimedia, 21(9):2184–2194, 2019.
  123. An extensive performance evaluation of full-reference HDR image quality metrics. Springer Quality and User Experience, 2(1):1–16, 2017.
  124. Perceptual image quality assessment: A survey. Springer Science China Information Sciences, 63(11):1–52, 2020.
  125. A psychovisual quality metric in free-energy principle. IEEE Transactions on Image Processing, 21(1):41–52, 2012.
  126. A feature-enriched completely blind image quality evaluator. IEEE Transactions on Image Processing, 24(8):2579–2591, 2015.
  127. The application of visual saliency models in objective image quality assessment: A statistical evaluation. IEEE Transactions on Neural Networks and Learning Systems, 27(6):1266–1278, 2015.
  128. Continual learning for blind image quality assessment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(1):1–15, 2022.
  129. Blind image quality assessment using a deep bilinear convolutional neural network. IEEE Transactions on Circuits and Systems for Video Technology, 30(1):36–47, 2020.
  130. Uncertainty-aware blind image quality assessment in the laboratory and wild. IEEE Transactions on Image Processing, 30(1):3474–3486, 2021.
  131. Quality-aware pre-trained models for blind image quality assessment. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 22302–22313, 2023.
  132. No-reference quality assessment for screen content images based on hybrid region features fusion. IEEE Transactions on Multimedia, 21(8):2057–2070, 2019.
  133. Uif: An objective quality assessment for underwater image enhancement. IEEE Transactions on Image Processing, 1:5456–5468, 2022.
  134. Visual quality assessment for super-resolved images: Database and method. IEEE Transactions on Image Processing, 28(7):3528–3541, 2019.
  135. No-reference quality assessment for 360-degree images by analysis of multifrequency information and local-global naturalness. IEEE Transactions on Circuits and Systems for Video Technology, 32(4):1778–1791, 2022.
  136. MetaIQA: Deep meta-learning for no-reference image quality assessment. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 14143–14152, 2020.
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Authors (1)
  1. Miaohui Wang (11 papers)

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