{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/koniq-10k-an-ecologically-valid-database-for","title":"KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment","arxiv_id":"1910.06180","date":"2019-10-14","proceeding":null,"authors":["Vlad Hosu","Hanhe Lin","Tamas Sziranyi","Dietmar Saupe"],"abstract":"Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512x384). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.","url_abs":"https://arxiv.org/abs/1910.06180v2","url_pdf":"https://arxiv.org/pdf/1910.06180v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"koniq-10k-an-ecologically-valid-database-for","repo_url":"https://github.com/subpic/koniq","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"koniq-10k-an-ecologically-valid-database-for","repo_url":"https://github.com/zhengyuzhao/koniq-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"no-reference-image-quality-assessment","task_name":"No-Reference Image Quality Assessment"},{"task_slug":"video-quality-assessment","task_name":"Video Quality Assessment"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[{"slug":"koniq-10k","name":"KonIQ-10k","full_name":"Konstanz Image Quality 10k Database"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality","task":"Video Quality Assessment","dataset":"MSU NR VQA Database","model":"KonCept512","rank_in_archive_order":16,"of":21,"metrics":{"KLCC":"0.6608","PLCC":"0.8464","SRCC":"0.8360","Type":"NR"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1910.06180","atlas_url":"https://app.syntology.ai/?focus=1910.06180","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.06180"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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