A Quality-Latency Frontier Changes with the Platform: Joining Published Image-Assessment Tables

Authors

  • Dominic Knight Author
  • Tyler Robertson Author
  • Evan Anderson Author

Abstract

Choosing an image-quality metric requires more than ordering its reported correlations. An exact-name join of five quality panels and two timing columns retains eleven complete methods from a pinned IQA-PyTorch snapshot. Five methods are nondominated for CPU latency and average reported SRCC, while only two remain on the corresponding GPU frontier. The quality-log-latency slopes are uncertain on both platforms. These conditional frontiers describe a published table join, with different quality and timing workloads, rather than a new controlled hardware benchmark or a universal recommendation.

Original publication: October 28, 2026.

References

  1. Yang, Sidi, et al. "MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment." arXiv preprint arXiv:2204.08958, 2022. https://arxiv.org/abs/2204.08958.
  2. Fang, Yuming; Zhu, Hanwei; Zeng, Yan; Ma, Kede; Wang, Zhou. (2020). Perceptual Quality Assessment of Smartphone Photography. CVPR 2020, 3677-3686. https://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.html.
  3. Cook, R. Dennis. (1977). Detection of Influential Observation in Linear Regression. Technometrics. https://doi.org/10.1080/00401706.1977.10489493.
  4. 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.
  5. Mittal, A., et al. "Making a “Completely Blind” Image Quality Analyzer" IEEE Signal Processing Letters, 2013. https://doi.org/10.1109/lsp.2012.2227726.
  6. Gu, Jinjin; Cai, Haoming; Chen, Haoyu; Ye, Xiaoxing; Ren, Jimmy; Dong, Chao. (2020). PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration. https://arxiv.org/abs/2007.12142.
  7. Chen, Chaofeng; Mo, Jiadi. (2022). IQA-PyTorch: PyTorch Toolbox for Image Quality Assessment. Software repository. https://github.com/chaofengc/IQA-PyTorch/tree/d47b7ac72ed25388371d0b637076c125cdb95a3c.
  8. Wilkinson, Mark D.; Dumontier, Michel; Aalbersberg, IJsbrand Jan; Appleton, Gabrielle; Axton, Myles; Baak, Arie; Blomberg, Niklas; Boiten, Jan-Willem; da Silva Santos, Luiz Bonino; Bourne, Philip E.; Bouwman, Jildau; Brookes, Anthony J.; Clark, Tim; Crosas, Mercè; Dillo, Ingrid; Dumon, Olivier; Edmunds, Scott; Evelo, Chris T.; Finkers, Richard; Gonzalez-Beltran, Alejandra; Gray, Alasdair J.G.; Groth, Paul; Goble, Carole; Grethe, Jeffrey S.; Heringa, Jaap; ’t Hoen, Peter A.C; Hooft, Rob; Kuhn, Tobias; Kok, Ruben; Kok, Joost; Lusher, Scott J.; Martone, Maryann E.; Mons, Albert; Packer, Abel L.; Persson, Bengt; Rocca-Serra, Philippe; Roos, Marco; van Schaik, Rene; Sansone, Susanna-Assunta; Schultes, Erik; Sengstag, Thierry; Slater, Ted; Strawn, George; Swertz, Morris A.; Thompson, Mark; van der Lei, Johan; van Mulligen, Erik; Velterop, Jan; Waagmeester, Andra; Wittenburg, Peter; Wolstencroft, Katherine; Zhao, Jun; Mons, Barend. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. https://doi.org/10.1038/sdata.2016.18.
  9. Talebi, Hossein and Peyman Milanfar. "NIMA: Neural Image Assessment" arXiv preprint arXiv:1709.05424, 2017. https://arxiv.org/abs/1709.05424.
  10. Mittal, A.; Moorthy, A. K.; Bovik, A. C.. (2012). No-Reference Image Quality Assessment in the Spatial Domain. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2012.2214050.
  11. 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.
  12. Murray, Naila; Marchesotti, Luca; Perronnin, Florent. (2012). AVA: A large-scale database for aesthetic visual analysis. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6247954.
  13. 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.
  14. Ponomarenko, Nikolay; Jin, Lina; Ieremeiev, Oleg; Lukin, Vladimir; Egiazarian, Karen; Astola, Jaakko; Vozel, Benoit; Chehdi, Kacem; Carli, Marco; Battisti, Federica; Jay Kuo, C.-C.. (2015). Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication. https://doi.org/10.1016/j.image.2014.10.009.
  15. Carroll, Raymond J., David Ruppert, Leonard A. Stefanski, and Ciprian M. Crainiceanu. “Measurement Error in Nonlinear Models.” 2006. https://doi.org/10.1201/9781420010138.
  16. Ying, Zhenqiang, et al. "From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality." arXiv preprint arXiv:1912.10088, 2019. https://arxiv.org/abs/1912.10088.
  17. 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.
  18. Demšar, Janez. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research. https://www.jmlr.org/papers/v7/demsar06a.html.
  19. Gebru, Timnit, et al. "Datasheets for datasets" Communications of the ACM, 2021. https://doi.org/10.1145/3458723.
  20. Gneiting, Tilmann; Raftery, Adrian E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association. https://doi.org/10.1198/016214506000001437.
  21. 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.
  22. Stevens, S. S.. (1946). On the Theory of Scales of Measurement. Science. https://doi.org/10.1126/science.103.2684.677.
  23. Meng, Zijie. (2026). Decoupling Semantics from Distortions: Multi-Scale Two-Stream Vision-Language Alignment for AI-Generated Image Quality Assessment. arXiv:2606.16799. https://arxiv.org/abs/2606.16799.
  24. KENDALL, M. G.. (1938). A NEW MEASURE OF RANK CORRELATION. Biometrika. https://doi.org/10.1093/biomet/30.1-2.81.
  25. Spearman, C.. (1904). The Proof and Measurement of Association between Two Things. The American Journal of Psychology. https://doi.org/10.2307/1412159.
  26. Mytkowicz, Todd; Diwan, Amer; Hauswirth, Matthias; Sweeney, Peter F.. (2009). Producing wrong data without doing anything obviously wrong!. Proceedings of the 14th international conference on Architectural support for programming languages and operating systems. https://doi.org/10.1145/1508244.1508275.
  27. Kalibera, Tomas; Jones, Richard. (2013). Rigorous benchmarking in reasonable time. Proceedings of the 2013 international symposium on memory management. https://doi.org/10.1145/2464157.2464160.
  28. Wasserstein, Ronald L.; Lazar, Nicole A.. (2016). The ASA Statement on p -Values: Context, Process, and Purpose. The American Statistician. https://doi.org/10.1080/00031305.2016.1154108.
  29. Willmott, CJ; Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research. https://doi.org/10.3354/cr030079.
  30. Williams, Samuel; Waterman, Andrew; Patterson, David. (2009). Roofline: an insightful visual performance model for multicore architectures. Communications of the ACM. https://doi.org/10.1145/1498765.1498785.

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Published

2026-10-28

Issue

Vol. 1 No. 1 (2026): Inaugural Issue

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Articles

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Copyright (c) 2026 Dominic Knight, Tyler Robertson, Evan Anderson

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