{"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/perceptual-quality-assessment-of-smartphone","title":"Perceptual Quality Assessment of Smartphone Photography","arxiv_id":null,"date":"2020-06-01","proceeding":"CVPR 2020 6","authors":["Yuming Fang"," Hanwei Zhu"," Yan Zeng"," Kede Ma"," Zhou Wang"],"abstract":"As smartphones become people's primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market. We conduct so far the most comprehensive study of perceptual quality assessment of smartphone photography. We introduce the Smartphone Photography Attribute and Quality (SPAQ) database, consisting of 11,125 pictures taken by 66 smartphones, where each image is attached with so far the richest annotations. Specifically, we collect a series of human opinions for each image, including image quality, image attributes (brightness, colorfulness, contrast, noisiness, and sharpness), and scene category labels (animal, cityscape, human, indoor scene, landscape, night scene, plant, still life, and others) in a well-controlled laboratory environment. The exchangeable image file format (EXIF) data for all images are also recorded to aid deeper analysis. We also make the first attempts using the database to train blind image quality assessment (BIQA) models constructed by baseline and multi-task deep neural networks. The results provide useful insights on how EXIF data, image attributes and high-level semantics interact with image quality, how next-generation BIQA models can be designed, and how better computational photography systems can be optimized on mobile devices. The database along with the proposed BIQA models are available at https://github.com/h4nwei/SPAQ.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2020/html/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Fang_Perceptual_Quality_Assessment_of_Smartphone_Photography_CVPR_2020_paper.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":"perceptual-quality-assessment-of-smartphone","repo_url":"https://github.com/h4nwei/SPAQ","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"image-quality-assessment","task_name":"Image Quality Assessment"},{"task_slug":"no-reference-image-quality-assessment","task_name":"No-Reference Image Quality Assessment"}],"methods":[],"datasets_introduced":[{"slug":"spaq","name":"SPAQ","full_name":"Smartphone Photography Attribute and Quality"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-quality-assessment-on-msu-nr-vqa","task":"Image Quality Assessment","dataset":"MSU NR VQA Database","model":"SPAQ MT-S","rank_in_archive_order":4,"of":10,"metrics":{"KLCC":"0.7186","PLCC":"0.8814","SRCC":"0.8822"},"uses_additional_data":false},{"leaderboard":"/sota/image-quality-assessment-on-msu-nr-vqa","task":"Image Quality Assessment","dataset":"MSU NR VQA Database","model":"SPAQ BL","rank_in_archive_order":5,"of":10,"metrics":{"KLCC":"0.7106","PLCC":"0.8855","SRCC":"0.8799"},"uses_additional_data":false},{"leaderboard":"/sota/image-quality-assessment-on-msu-nr-vqa","task":"Image Quality Assessment","dataset":"MSU NR VQA Database","model":"SPAQ MT-A","rank_in_archive_order":6,"of":10,"metrics":{"KLCC":"0.7148","PLCC":"0.8824","SRCC":"0.8794"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality","task":"Video Quality Assessment","dataset":"MSU NR VQA Database","model":"SPAQ MT-S","rank_in_archive_order":9,"of":21,"metrics":{"KLCC":"0.7186","PLCC":"0.8814","SRCC":"0.8822","Type":"NR"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality","task":"Video Quality Assessment","dataset":"MSU NR VQA Database","model":"SPAQ BL","rank_in_archive_order":10,"of":21,"metrics":{"KLCC":"0.7106","PLCC":"0.8855","SRCC":"0.8799","Type":"NR"},"uses_additional_data":false},{"leaderboard":"/sota/video-quality-assessment-on-msu-video-quality","task":"Video Quality Assessment","dataset":"MSU NR VQA Database","model":"SPAQ MT-A","rank_in_archive_order":11,"of":21,"metrics":{"KLCC":"0.7148","PLCC":"0.8824","SRCC":"0.8794","Type":"NR"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}