{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:V2DZVOPHMUS75QMYPMAIL2UK2L","short_pith_number":"pith:V2DZVOPH","schema_version":"1.0","canonical_sha256":"ae879ab9e76525fec1987b0085ea8ad2db7c5b44fe019fd7207030648959c37a","source":{"kind":"arxiv","id":"1908.06911","version":1},"attestation_state":"computed","paper":{"title":"Algorithm Selection for Image Quality Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dietmar Saupe, Hanhe Lin, Markus Wagner, Shujun Li","submitted_at":"2019-08-19T16:15:22Z","abstract_excerpt":"Subjective perceptual image quality can be assessed in lab studies by human observers. Objective image quality assessment (IQA) refers to algorithms for estimation of the mean subjective quality ratings. Many such methods have been proposed, both for blind IQA in which no original reference image is available as well as for the full-reference case. We compared 8 state-of-the-art algorithms for blind IQA and showed that an oracle, able to predict the best performing method for any given input image, yields a hybrid method that could outperform even the best single existing method by a large mar"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1908.06911","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-19T16:15:22Z","cross_cats_sorted":[],"title_canon_sha256":"e12f62bece95cec3a7ea1db705f6ce31cfcc29b422b55419f1c2854136fa53ca","abstract_canon_sha256":"7875d14a7020968ea05262fcd38bf2ef4e7a10e30bc0febfc29dbca76553750f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:58:20.856863Z","signature_b64":"Nms/7mXuRx2pu8eNywCxKFuLPekvkx5sxaKqWieFXbCz1b9TDc43XSdDkYJa5GdoVDnASugn1x7lTeEA2mDxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae879ab9e76525fec1987b0085ea8ad2db7c5b44fe019fd7207030648959c37a","last_reissued_at":"2026-07-04T23:58:20.856511Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:58:20.856511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Algorithm Selection for Image Quality Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dietmar Saupe, Hanhe Lin, Markus Wagner, Shujun Li","submitted_at":"2019-08-19T16:15:22Z","abstract_excerpt":"Subjective perceptual image quality can be assessed in lab studies by human observers. Objective image quality assessment (IQA) refers to algorithms for estimation of the mean subjective quality ratings. Many such methods have been proposed, both for blind IQA in which no original reference image is available as well as for the full-reference case. We compared 8 state-of-the-art algorithms for blind IQA and showed that an oracle, able to predict the best performing method for any given input image, yields a hybrid method that could outperform even the best single existing method by a large mar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06911","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.06911/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1908.06911","created_at":"2026-07-04T23:58:20.856571+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.06911v1","created_at":"2026-07-04T23:58:20.856571+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06911","created_at":"2026-07-04T23:58:20.856571+00:00"},{"alias_kind":"pith_short_12","alias_value":"V2DZVOPHMUS7","created_at":"2026-07-04T23:58:20.856571+00:00"},{"alias_kind":"pith_short_16","alias_value":"V2DZVOPHMUS75QMY","created_at":"2026-07-04T23:58:20.856571+00:00"},{"alias_kind":"pith_short_8","alias_value":"V2DZVOPH","created_at":"2026-07-04T23:58:20.856571+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L","json":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L.json","graph_json":"https://pith.science/api/pith-number/V2DZVOPHMUS75QMYPMAIL2UK2L/graph.json","events_json":"https://pith.science/api/pith-number/V2DZVOPHMUS75QMYPMAIL2UK2L/events.json","paper":"https://pith.science/paper/V2DZVOPH"},"agent_actions":{"view_html":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L","download_json":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L.json","view_paper":"https://pith.science/paper/V2DZVOPH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.06911&json=true","fetch_graph":"https://pith.science/api/pith-number/V2DZVOPHMUS75QMYPMAIL2UK2L/graph.json","fetch_events":"https://pith.science/api/pith-number/V2DZVOPHMUS75QMYPMAIL2UK2L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L/action/storage_attestation","attest_author":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L/action/author_attestation","sign_citation":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L/action/citation_signature","submit_replication":"https://pith.science/pith/V2DZVOPHMUS75QMYPMAIL2UK2L/action/replication_record"}},"created_at":"2026-07-04T23:58:20.856571+00:00","updated_at":"2026-07-04T23:58:20.856571+00:00"}