{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZAOGAOJZH5F7VURFOXJI72G4KK","short_pith_number":"pith:ZAOGAOJZ","schema_version":"1.0","canonical_sha256":"c81c6039393f4bfad22575d28fe8dc5295981445cee768b18185c1c1d5472596","source":{"kind":"arxiv","id":"2407.21363","version":2},"attestation_state":"computed","paper":{"title":"ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Guangtao Zhai, Huiyu Duan, Liu Yang, Patrick Le Callet, Xilei Zhu, Xiongkuo Min","submitted_at":"2024-07-31T06:20:21Z","abstract_excerpt":"With the development of eXtended Reality (XR), photo capturing and display technology based on head-mounted displays (HMDs) have experienced significant advancements and gained considerable attention. Egocentric spatial images and videos are emerging as a compelling form of stereoscopic XR content. The assessment for the Quality of Experience (QoE) of XR content is important to ensure a high-quality viewing experience. Different from traditional 2D images, egocentric spatial images present challenges for perceptual quality assessment due to their special shooting, processing methods, and stere"},"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":"2407.21363","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-31T06:20:21Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"b8c05947f3382a73f57ddbdcd864c655d18fab02847c84364e546be0b3747fce","abstract_canon_sha256":"fa9fa3fe9d51c29300a15898bd756f6d1e0a4f287f84386050534570c16e3c68"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:17:48.443367Z","signature_b64":"+injRot54nszWJZs/XShiv71ASkze0ekndqO8JuvpbVvQONUGWmrysVwcVATZJFPG9AmWj2t4bVOwydfuB62DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c81c6039393f4bfad22575d28fe8dc5295981445cee768b18185c1c1d5472596","last_reissued_at":"2026-07-05T10:17:48.442783Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:17:48.442783Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.CV","authors_text":"Guangtao Zhai, Huiyu Duan, Liu Yang, Patrick Le Callet, Xilei Zhu, Xiongkuo Min","submitted_at":"2024-07-31T06:20:21Z","abstract_excerpt":"With the development of eXtended Reality (XR), photo capturing and display technology based on head-mounted displays (HMDs) have experienced significant advancements and gained considerable attention. Egocentric spatial images and videos are emerging as a compelling form of stereoscopic XR content. The assessment for the Quality of Experience (QoE) of XR content is important to ensure a high-quality viewing experience. Different from traditional 2D images, egocentric spatial images present challenges for perceptual quality assessment due to their special shooting, processing methods, and stere"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.21363","kind":"arxiv","version":2},"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/2407.21363/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":"2407.21363","created_at":"2026-07-05T10:17:48.442847+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.21363v2","created_at":"2026-07-05T10:17:48.442847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.21363","created_at":"2026-07-05T10:17:48.442847+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZAOGAOJZH5F7","created_at":"2026-07-05T10:17:48.442847+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZAOGAOJZH5F7VURF","created_at":"2026-07-05T10:17:48.442847+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZAOGAOJZ","created_at":"2026-07-05T10:17:48.442847+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.21925","citing_title":"Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images","ref_index":24,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK","json":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK.json","graph_json":"https://pith.science/api/pith-number/ZAOGAOJZH5F7VURFOXJI72G4KK/graph.json","events_json":"https://pith.science/api/pith-number/ZAOGAOJZH5F7VURFOXJI72G4KK/events.json","paper":"https://pith.science/paper/ZAOGAOJZ"},"agent_actions":{"view_html":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK","download_json":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK.json","view_paper":"https://pith.science/paper/ZAOGAOJZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.21363&json=true","fetch_graph":"https://pith.science/api/pith-number/ZAOGAOJZH5F7VURFOXJI72G4KK/graph.json","fetch_events":"https://pith.science/api/pith-number/ZAOGAOJZH5F7VURFOXJI72G4KK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK/action/storage_attestation","attest_author":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK/action/author_attestation","sign_citation":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK/action/citation_signature","submit_replication":"https://pith.science/pith/ZAOGAOJZH5F7VURFOXJI72G4KK/action/replication_record"}},"created_at":"2026-07-05T10:17:48.442847+00:00","updated_at":"2026-07-05T10:17:48.442847+00:00"}