Rank Thresholds and the Frequency of Quality-Alignment Discordance
Abstract
How often quality and alignment appear discordant depends on the threshold used to define opposite outcomes. In AGIQA-3K, opposite outer-quartile classifications identify 19 high-quality, low-alignment rows and five with the reverse pattern. Expanding each tail to forty percent raises the counts to 139 and 108. This secondary analysis uses empirical score ranks across 2,982 rows. It treats thresholds as declared descriptive rules, showing that a positive overall relationship does not eliminate disagreement and that no single cutoff supplies a universal failure definition.
References
- Meng, Zijie. "Decoupling Semantics from Distortions: Multi-Scale Two-Stream Vision-Language Alignment for AI-Generated Image Quality Assessment." arXiv preprint arXiv:2606.16799 (2026).
- White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
- Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
- Greenland, Sander, Judea Pearl, and James M. Robins. “Causal Diagrams for Epidemiologic Research.” Epidemiology, vol. 10, no. 1, 1999, pp. 37-48. https://doi.org/10.1097/00001648-199901000-00008.
- Benjamini, Yoav and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society Series B: Statistical Methodology, vol. 57, no. 1, 1995, pp. 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.
- Roberts, David R., et al. “Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure.” Ecography, vol. 40, no. 8, 2017, pp. 913-929. https://doi.org/10.1111/ecog.02881.
- Varma, Sudhir and Richard Simon. “Bias in error estimation when using cross-validation for model selection.” BMC Bioinformatics, vol. 7, no. 1, 2006. https://doi.org/10.1186/1471-2105-7-91.
- Wilkinson, Mark D., et al. "The FAIR Guiding Principles for Scientific Data Management and Stewardship." Scientific Data, vol. 3, 2016, article 160018. https://doi.org/10.1038/sdata.2016.18.
- Sculley, D., et al. "Hidden Technical Debt in Machine Learning Systems." Advances in Neural Information Processing Systems, vol. 28, 2015. https://papers.nips.cc/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html
- Bland, J. Martin, and Douglas G. Altman. "Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement." The Lancet, vol. 327, no. 8476, 1986, pp. 307-310. https://doi.org/10.1016/S0140-6736(86)90837-8.
- Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, PMLR, vol. 70, 2017, pp. 1321–1330. https://proceedings.mlr.press/v70/guo17a.html.
- Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017. https://arxiv.org/abs/1705.08500.
- Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019. https://arxiv.org/abs/1906.02530.
- Li, Chunyi, et al.. "AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment" arXiv preprint arXiv:2306.04717, 2023. https://arxiv.org/abs/2306.04717.
- Radford, Alec, et al.. "Learning Transferable Visual Models From Natural Language Supervision" arXiv preprint arXiv:2103.00020, 2021. https://arxiv.org/abs/2103.00020.
- Ke, Junjie, et al.. "MUSIQ: Multi-scale Image Quality Transformer" arXiv preprint arXiv:2108.05997, 2021. https://arxiv.org/abs/2108.05997.
- Hosu, Vlad, et al.. "KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment" arXiv preprint arXiv:1910.06180, 2019. https://arxiv.org/abs/1910.06180.
- Lin, Hanhe, et al.. "KADID-10k: A Large-scale Artificially Distorted IQA Database" 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), 2019. https://doi.org/10.1109/qomex.2019.8743252.
- Wang, Zhou, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. "Image Quality Assessment: From Error Visibility to Structural Similarity." IEEE Transactions on Image Processing, vol. 13, no. 4, 2004, pp. 600-612. https://doi.org/10.1109/TIP.2003.819861.
- Zhang, Richard, et al.. "The Unreasonable Effectiveness of Deep Features as a Perceptual Metric" arXiv preprint arXiv:1801.03924, 2018. https://arxiv.org/abs/1801.03924.
- Mittal, A., et al.. "Making a “Completely Blind” Image Quality Analyzer" IEEE Signal Processing Letters, 2013. https://doi.org/10.1109/lsp.2012.2227726.
- Talebi, Hossein and Peyman Milanfar. "NIMA: Neural Image Assessment" arXiv preprint arXiv:1709.05424, 2017. https://arxiv.org/abs/1709.05424.
- Kirstain, Yuval, et al.. "Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation" arXiv preprint arXiv:2305.01569, 2023. https://arxiv.org/abs/2305.01569.
- Wu, Xiaoshi, et al.. "Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis" arXiv preprint arXiv:2306.09341, 2023. https://arxiv.org/abs/2306.09341.
- Caron, Mathilde, et al.. "Emerging Properties in Self-Supervised Vision Transformers" arXiv preprint arXiv:2104.14294, 2021. https://arxiv.org/abs/2104.14294.
- Ruiz, Nataniel, et al.. "DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation" arXiv preprint arXiv:2208.12242, 2022. https://arxiv.org/abs/2208.12242.
- Ye, Hu, et al.. "IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models" arXiv preprint arXiv:2308.06721, 2023. https://arxiv.org/abs/2308.06721.
- Heusel, Martin, et al.. "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium" arXiv preprint arXiv:1706.08500, 2017. https://arxiv.org/abs/1706.08500.
- Gebru, Timnit, et al.. "Datasheets for datasets" Communications of the ACM, 2021. https://doi.org/10.1145/3458723.
- Pedregosa, Fabian, et al. "Scikit-learn: Machine Learning in Python." Journal of Machine Learning Research, vol. 12, 2011, pp. 2825-2830. https://jmlr.org/papers/v12/pedregosa11a.html.
Published
Issue
Section
License
Copyright (c) 2026 James Pearson, Sean Griffin, Timothy Clark

This work is licensed under a Creative Commons Attribution 4.0 International License.