{"url":"/dataset/spaq","name":"SPAQ","full_name":"Smartphone Photography Attribute and Quality","description_markdown":"The Smartphone Photography Attribute and Quality (SPAQ) dataset is a comprehensive database for the perceptual quality assessment of smartphone photography. It was introduced in a paper titled \"Perceptual Quality Assessment of Smartphone Photography\" presented at the IEEE Conference on Computer Vision and Pattern Recognition in 2020.\r\n\r\nThe SPAQ dataset consists of **11,125 pictures** taken by **66 smartphones**. Each image in the dataset is attached with rich annotations, including:\r\n- Image quality\r\n- Image attributes (brightness, colorfulness, contrast, noisiness, and sharpness)\r\n- Scene category labels (animal, cityscape, human, indoor scene, landscape, night scene, plant, still life, and others)\r\n- Exchangeable image file format (EXIF) data\r\n\r\nThese annotations were collected in a well-controlled laboratory environment. The dataset has been used to train blind image quality assessment (BIQA) models, providing insights into how EXIF data, image attributes, and high-level semantics interact with image quality. The SPAQ dataset and the proposed BIQA models are available on GitHub.","description_withheld":null,"homepage":"https://github.com/h4nwei/SPAQ","introduced_date":"2020-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/perceptual-quality-assessment-of-smartphone","title":"Perceptual Quality Assessment of Smartphone Photography","first_author":"Yuming Fang","url":null},"license":null,"modalities":[],"tasks":[{"name":"Image Quality Assessment","url":"/task/image-quality-assessment","datasets_with_task":"/datasets/task/image-quality-assessment"},{"name":"No-Reference Image Quality Assessment","url":"/task/no-reference-image-quality-assessment","datasets_with_task":"/datasets/task/no-reference-image-quality-assessment"}],"languages":[],"variants":["SPAQ"],"data_loaders":[],"num_papers_in_archive":86,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/no-reference-image-quality-assessment-on-spaq","task":"No-Reference Image Quality Assessment","dataset_variant":"SPAQ","rows":1,"metrics":["PLCC","SRCC"],"first_row_in_archive_order":{"model":"RvTC (image-only)","paper":"/paper/language-integration-in-fine-tuning","metrics":{"PLCC":"0.93","SRCC":"0.93"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-quality-assessment-on-spaq","task":"Image Quality Assessment","dataset_variant":"SPAQ","rows":0,"metrics":["PLCC","SRCC"],"first_row_in_archive_order":null,"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/language-integration-in-fine-tuning","title":"Language Integration in Fine-Tuning Multimodal Large Language Models for Image-Based Regression","date":"2025-07-20","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}