{"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/massive-online-crowdsourced-study-of","title":"Massive Online Crowdsourced Study of Subjective and Objective Picture Quality","arxiv_id":"1511.02919","date":"2015-11-09","proceeding":null,"authors":["Deepti Ghadiyaram","Alan C. Bovik"],"abstract":"Most publicly available image quality databases have been created under\nhighly controlled conditions by introducing graded simulated distortions onto\nhigh-quality photographs. However, images captured using typical real-world\nmobile camera devices are usually afflicted by complex mixtures of multiple\ndistortions, which are not necessarily well-modeled by the synthetic\ndistortions found in existing databases. The originators of existing legacy\ndatabases usually conducted human psychometric studies to obtain statistically\nmeaningful sets of human opinion scores on images in a stringently controlled\nvisual environment, resulting in small data collections relative to other kinds\nof image analysis databases. Towards overcoming these limitations, we designed\nand created a new database that we call the LIVE In the Wild Image Quality\nChallenge Database, which contains widely diverse authentic image distortions\non a large number of images captured using a representative variety of modern\nmobile devices. We also designed and implemented a new online crowdsourcing\nsystem, which we have used to conduct a very large-scale, multi-month image\nquality assessment subjective study. Our database consists of over 350000\nopinion scores on 1162 images evaluated by over 7000 unique human observers.\nDespite the lack of control over the experimental environments of the numerous\nstudy participants, we demonstrate excellent internal consistency of the\nsubjective dataset. We also evaluate several top-performing blind Image Quality\nAssessment algorithms on it and present insights on how mixtures of distortions\nchallenge both end users as well as automatic perceptual quality prediction\nmodels.","url_abs":"http://arxiv.org/abs/1511.02919v1","url_pdf":"http://arxiv.org/pdf/1511.02919v1.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":[],"tasks":[{"task_slug":"blind-image-quality-assessment","task_name":"Blind Image Quality Assessment"},{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"small-data","task_name":"Small Data Image Classification"}],"methods":[],"datasets_introduced":[{"slug":"live-itw","name":"LIVE-itw","full_name":"LIVE In the Wild Image Quality Challenge Database"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.02919","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}