{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:O4XFY4IPUNQEJ4PZVQCLMSHXBR","short_pith_number":"pith:O4XFY4IP","schema_version":"1.0","canonical_sha256":"772e5c710fa36044f1f9ac04b648f70c7502861e081a4bf3202654bc886be258","source":{"kind":"arxiv","id":"2605.22192","version":1},"attestation_state":"computed","paper":{"title":"Ultra-High-Definition Image Quality Assessment via Graph Representation Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Enqi Chen, Ming Huang, Qiurui Sun, Shaode Yu, Songnan Zhao, Xuemin Ren, Zhicheng Zhang","submitted_at":"2026-05-21T08:57:59Z","abstract_excerpt":"Blind image quality assessment (BIQA) for ultrahighdefinition (UHD) images remains challenging because native-resolution inference is computationally expensive, whereas aggressive resizing or isolated cropping may suppress scale-sensitive distortions and weaken the relationship between local artifacts and global scene context. This paper aims to improve UHD-BIQA by explicitly modeling the structural dependencies among sampled image regions rather than treating them as independent views, and a graph representation learning framework UHD-GCN-BIQA is proposed. The framework samples aspect-ratio-a"},"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":"2605.22192","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-21T08:57:59Z","cross_cats_sorted":[],"title_canon_sha256":"fc4082b94962858fa56b3458f94e13f34c4eb49c9a11c7ed983b1131b561b80e","abstract_canon_sha256":"b3269f56dceaa1c4b1197b38fa9d476608f48d825f9a71002e2b788c5d1c4134"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:04:31.157489Z","signature_b64":"XOcYIX0D5HbFtAHIcQssN2uopFDj0++WiDjg/ehLP3JMwEr3Zrz8ZEibJhVR16FGXhutF5HRDEIQyqjNI6p1Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"772e5c710fa36044f1f9ac04b648f70c7502861e081a4bf3202654bc886be258","last_reissued_at":"2026-05-22T01:04:31.156658Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:04:31.156658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ultra-High-Definition Image Quality Assessment via Graph Representation Learning","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Enqi Chen, Ming Huang, Qiurui Sun, Shaode Yu, Songnan Zhao, Xuemin Ren, Zhicheng Zhang","submitted_at":"2026-05-21T08:57:59Z","abstract_excerpt":"Blind image quality assessment (BIQA) for ultrahighdefinition (UHD) images remains challenging because native-resolution inference is computationally expensive, whereas aggressive resizing or isolated cropping may suppress scale-sensitive distortions and weaken the relationship between local artifacts and global scene context. This paper aims to improve UHD-BIQA by explicitly modeling the structural dependencies among sampled image regions rather than treating them as independent views, and a graph representation learning framework UHD-GCN-BIQA is proposed. The framework samples aspect-ratio-a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22192","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/2605.22192/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":"2605.22192","created_at":"2026-05-22T01:04:31.156799+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.22192v1","created_at":"2026-05-22T01:04:31.156799+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22192","created_at":"2026-05-22T01:04:31.156799+00:00"},{"alias_kind":"pith_short_12","alias_value":"O4XFY4IPUNQE","created_at":"2026-05-22T01:04:31.156799+00:00"},{"alias_kind":"pith_short_16","alias_value":"O4XFY4IPUNQEJ4PZ","created_at":"2026-05-22T01:04:31.156799+00:00"},{"alias_kind":"pith_short_8","alias_value":"O4XFY4IP","created_at":"2026-05-22T01:04:31.156799+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/O4XFY4IPUNQEJ4PZVQCLMSHXBR","json":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR.json","graph_json":"https://pith.science/api/pith-number/O4XFY4IPUNQEJ4PZVQCLMSHXBR/graph.json","events_json":"https://pith.science/api/pith-number/O4XFY4IPUNQEJ4PZVQCLMSHXBR/events.json","paper":"https://pith.science/paper/O4XFY4IP"},"agent_actions":{"view_html":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR","download_json":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR.json","view_paper":"https://pith.science/paper/O4XFY4IP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.22192&json=true","fetch_graph":"https://pith.science/api/pith-number/O4XFY4IPUNQEJ4PZVQCLMSHXBR/graph.json","fetch_events":"https://pith.science/api/pith-number/O4XFY4IPUNQEJ4PZVQCLMSHXBR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR/action/storage_attestation","attest_author":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR/action/author_attestation","sign_citation":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR/action/citation_signature","submit_replication":"https://pith.science/pith/O4XFY4IPUNQEJ4PZVQCLMSHXBR/action/replication_record"}},"created_at":"2026-05-22T01:04:31.156799+00:00","updated_at":"2026-05-22T01:04:31.156799+00:00"}