{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T11:05:03Z","timestamp":1790247903395,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Provincial Key Research and Development Program","award":["2025CXPT086"],"award-info":[{"award-number":["2025CXPT086"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fish iris images provide morphological information for biometric recognition and biological observation, but underwater imaging often introduces blur, reflection, occlusion, low contrast, and boundary ambiguity. Conventional no-reference image quality assessment provides global perceptual scores but cannot determine whether local iris boundaries are reliably traceable for annotation. We formulate fish iris quality control as a three-level annotatability assessment task for pre-annotation screening, enabling direct annotation, further review, or exclusion\/reacquisition. Under limited supervision, the framework learns complementary quality evidence from 53-dimensional handcrafted quality descriptors and 384-dimensional embeddings extracted from a frozen pretrained MANIQA (Multi-Dimension Attention Network for No-Reference Image Quality Assessment) model. LateAvg performs lightweight decision-level integration by equally averaging branch posterior probabilities. On 1500 self-collected underwater images evaluated by fish-group five-fold cross-validation, the framework achieves 0.8574 accuracy and 0.8510 Macro-F1. It achieves the highest mean Accuracy, Macro-F1, and Weighted-F1 among evaluated methods, outperforming scalar IQA baselines such as NIQE-score and TOPIQ-score while remaining statistically comparable to trainable integration and late fine-tuning approaches. These results support boundary-traceability-based pre-annotation screening. With cached quality evidence, the framework introduces only a lightweight decision stage to support efficient annotation-resource allocation in biological dataset construction.<\/jats:p>","DOI":"10.3390\/s26196053","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:16:28Z","timestamp":1790244988000},"page":"6053","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Three-Level Fish Iris Image Annotatability Assessment for Pre-Annotation Quality Control Based on Boundary Traceability"],"prefix":"10.3390","volume":"26","author":[{"given":"Ziying","family":"Yang","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Engineering, Shandong University, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8229-1485","authenticated-orcid":false,"given":"Yuyang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Engineering, Shandong University, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6197-5245","authenticated-orcid":false,"given":"Ying","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Engineering, Shandong University, Weihai 264209, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4356","DOI":"10.1109\/TII.2020.3006933","article-title":"Towards Fish Individuality-Based Aquaculture","volume":"17","author":"Schraml","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102689","DOI":"10.1016\/j.ecoinf.2024.102689","article-title":"A Video Object Segmentation-Based Fish Individual Recognition Method for Underwater Complex Environments","volume":"82","author":"Zheng","year":"2024","journal-title":"Ecol. Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201cCompletely Blind\u201d Image Quality Analyzer","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2404","DOI":"10.1109\/TIP.2024.3378466","article-title":"TOPIQ: A Top-Down Approach from Semantics to Distortions for Image Quality Assessment","volume":"33","author":"Chen","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","first-page":"48","article-title":"Evaluation of Retinal Image Quality Assessment Networks in Different Color-Spaces","volume":"Volume 11764","author":"Shen","year":"2019","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compmedimag.2008.11.002","article-title":"Lesion Border Detection in Dermoscopy Images","volume":"33","author":"Celebi","year":"2009","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nowak, S., and R\u00fcger, S. (2010). How Reliable Are Annotations via Crowdsourcing: A Study about Inter-Annotator Agreement for Multi-Label Image Annotation. Proceedings of the International Conference on Multimedia Information Retrieval, ACM.","DOI":"10.1145\/1743384.1743478"},{"key":"ref_8","first-page":"1297","article-title":"Learning from Crowds","volume":"11","author":"Raykar","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_9","unstructured":"Welinder, P., Branson, S., Perona, P., and Belongie, S. (2010). The Multidimensional Wisdom of Crowds. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_10","unstructured":"Northcutt, C.G., Athalye, A., and Mueller, J. (2021). Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks. Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, NeurIPS."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3711118","article-title":"Data-Centric Artificial Intelligence: A Survey","volume":"57","author":"Zha","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.1613\/jair.1.12125","article-title":"Confident Learning: Estimating Uncertainty in Dataset Labels","volume":"70","author":"Northcutt","year":"2021","journal-title":"J. Artif. Intell. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8135","DOI":"10.1109\/TNNLS.2022.3152527","article-title":"Learning from Noisy Labels with Deep Neural Networks: A Survey","volume":"34","author":"Song","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ma, J., Ushiku, Y., and Sagara, M. (2022). The Effect of Improving Annotation Quality on Object Detection Datasets: A Preliminary Study. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW56347.2022.00532"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bilal, M., Podishetti, R., Koval, L., Gaafar, M.A., Grossmann, D., and Bregulla, M. (2024). The Effect of Annotation Quality on Wear Semantic Segmentation by CNN. Sensors, 24.","DOI":"10.3390\/s24154777"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/s13640-024-00644-1","article-title":"Utility-Based Performance Evaluation of Biometric Sample Quality Measures","volume":"2024","author":"Henniger","year":"2024","journal-title":"J. Image Video Proc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"121282","DOI":"10.1016\/j.eswa.2023.121282","article-title":"Interactive Medical Image Annotation Using Improved Attention U-Net with Compound Geodesic Distance","volume":"237","author":"Zhang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, S., Wu, T., Shi, S., Lao, S., Gong, Y., Cao, M., Wang, J., and Yang, Y. (2022). MANIQA: Multi-Dimension Attention Network for No-Reference Image Quality Assessment. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW56347.2022.00126"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/LSP.2010.2043888","article-title":"A Two-Step Framework for Constructing Blind Image Quality Indices","volume":"17","author":"Moorthy","year":"2010","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1109\/TIP.2011.2147325","article-title":"Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality","volume":"20","author":"Moorthy","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3339","DOI":"10.1109\/TIP.2012.2191563","article-title":"Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain","volume":"21","author":"Saad","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-Reference Image Quality Assessment in the Spatial Domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1109\/TIP.2015.2426416","article-title":"A Feature-Enriched Completely Blind Image Quality Evaluator","volume":"24","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Venkatanath, N., Praneeth, D., Maruthi Chandrasekhar, B.H., Channappayya, S.S., and Medasani, S.S. (2015). Blind Image Quality Evaluation Using Perception Based Features. Proceedings of the 2015 Twenty First National Conference on Communications (NCC), IEEE.","DOI":"10.1109\/NCC.2015.7084843"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6062","DOI":"10.1109\/TIP.2015.2491020","article-title":"An Underwater Color Image Quality Evaluation Metric","volume":"24","author":"Yang","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/JOE.2015.2469915","article-title":"Human-Visual-System-Inspired Underwater Image Quality Measures","volume":"41","author":"Panetta","year":"2016","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e70068","DOI":"10.1049\/ipr2.70068","article-title":"Underwater Image Quality Evaluation: A Comprehensive Review","volume":"19","author":"Shen","year":"2025","journal-title":"IET Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kang, L., Ye, P., Li, Y., and Doermann, D. (2014). Convolutional Neural Networks for No-Reference Image Quality Assessment. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2014.224"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kang, L., Ye, P., Li, Y., and Doermann, D. (2015). Simultaneous Estimation of Image Quality and Distortion via Multi-Task Convolutional Neural Networks. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP.2015.7351311"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JSTSP.2016.2639328","article-title":"Fully Deep Blind Image Quality Predictor","volume":"11","author":"Kim","year":"2017","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TIP.2017.2760518","article-title":"Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment","volume":"27","author":"Bosse","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1109\/TIP.2017.2774045","article-title":"End-to-End Blind Image Quality Assessment Using Deep Neural Networks","volume":"27","author":"Ma","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, X., Van De Weijer, J., and Bagdanov, A.D. (2017). RankIQA: Learning from Rankings for No-Reference Image Quality Assessment. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.118"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/TCSVT.2018.2886771","article-title":"Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network","volume":"30","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., and Zhang, Y. (2020). Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00372"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., and Bovik, A. (2020). From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR42600.2020.00363"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3474","DOI":"10.1109\/TIP.2021.3061932","article-title":"Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Golestaneh, S.A., Dadsetan, S., and Kitani, K.M. (2022). No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency. Proceedings of the 2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV51458.2022.00404"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1109\/TIP.2022.3144892","article-title":"VCRNet: Visual Compensation Restoration Network for No-Reference Image Quality Assessment","volume":"31","author":"Pan","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7518","DOI":"10.1109\/TCSVT.2022.3188991","article-title":"DACNN: Blind Image Quality Assessment via a Distortion-Aware Convolutional Neural Network","volume":"32","author":"Pan","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1609\/aaai.v38i5.28285","article-title":"Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning","volume":"38","author":"Shi","year":"2024","journal-title":"AAAI"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"You, J., and Korhonen, J. (2021). Transformer for Image Quality Assessment. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP42928.2021.9506075"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ke, J., Wang, Q., Wang, Y., Milanfar, P., and Yang, F. (2021). MUSIQ: Multi-Scale Image Quality Transformer. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00510"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, J., Fan, H., Hou, X., Xu, Y., Li, T., Lu, X., and Fu, L. (2022). MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE.","DOI":"10.1109\/CVPRW56347.2022.00133"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, Y., Lei, Z., and Li, C. (2024). No-Reference Image Quality Assessment Combining Swin-Transformer and Natural Scene Statistics. Sensors, 24.","DOI":"10.3390\/s24165221"},{"key":"ref_46","first-page":"1","article-title":"Deep Multimodal Data Fusion","volume":"56","author":"Zhao","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4041","DOI":"10.1109\/TIP.2020.2967829","article-title":"KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment","volume":"29","author":"Hosu","year":"2020","journal-title":"IEEE Trans. Image Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6053\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T10:19:10Z","timestamp":1790245150000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":47,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196053"],"URL":"https:\/\/doi.org\/10.3390\/s26196053","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}