{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DA53GV2HPBKSMAO2SPL3HGFB6M","short_pith_number":"pith:DA53GV2H","schema_version":"1.0","canonical_sha256":"183bb3574778552601da93d7b398a1f333b429fc060c878271065ca5b7bce4fd","source":{"kind":"arxiv","id":"2506.11546","version":1},"attestation_state":"computed","paper":{"title":"CGVQM+D: Computer Graphics Video Quality Metric and Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Akshay Jindal, Anton Kaplanyan, Anton Sochenov, Manu Mathew Thomas, Nabil Sadaka","submitted_at":"2025-06-13T07:59:55Z","abstract_excerpt":"While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference quality metrics perform sub-optimally on these distortions, with a maximum Pearson correlation of 0."},"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":"2506.11546","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.GR","submitted_at":"2025-06-13T07:59:55Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"76f4a82255384871edcb21b17edaa2f2b7afa8b04fb88066cbb2eedf3f2d321a","abstract_canon_sha256":"0be1da5e482c49813542c453a1db0fcaf859096fb335bee11d9e0e30e6da29bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:06.461281Z","signature_b64":"LcWY7kiDr+e4USDKB/foIj625A9LK/BBE+bvT1utWGTkb7OWye5L+VEzmDKyX5qdFovs8X/n1Fq2/+8N2DnIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"183bb3574778552601da93d7b398a1f333b429fc060c878271065ca5b7bce4fd","last_reissued_at":"2026-07-05T11:21:06.460747Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:06.460747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CGVQM+D: Computer Graphics Video Quality Metric and Dataset","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.GR","authors_text":"Akshay Jindal, Anton Kaplanyan, Anton Sochenov, Manu Mathew Thomas, Nabil Sadaka","submitted_at":"2025-06-13T07:59:55Z","abstract_excerpt":"While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference quality metrics perform sub-optimally on these distortions, with a maximum Pearson correlation of 0."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11546","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/2506.11546/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":"2506.11546","created_at":"2026-07-05T11:21:06.460814+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.11546v1","created_at":"2026-07-05T11:21:06.460814+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11546","created_at":"2026-07-05T11:21:06.460814+00:00"},{"alias_kind":"pith_short_12","alias_value":"DA53GV2HPBKS","created_at":"2026-07-05T11:21:06.460814+00:00"},{"alias_kind":"pith_short_16","alias_value":"DA53GV2HPBKSMAO2","created_at":"2026-07-05T11:21:06.460814+00:00"},{"alias_kind":"pith_short_8","alias_value":"DA53GV2H","created_at":"2026-07-05T11:21:06.460814+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09330","citing_title":"Dynamic XR Rendering Offloading Based on Feature-Based Quality Assessment","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M","json":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M.json","graph_json":"https://pith.science/api/pith-number/DA53GV2HPBKSMAO2SPL3HGFB6M/graph.json","events_json":"https://pith.science/api/pith-number/DA53GV2HPBKSMAO2SPL3HGFB6M/events.json","paper":"https://pith.science/paper/DA53GV2H"},"agent_actions":{"view_html":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M","download_json":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M.json","view_paper":"https://pith.science/paper/DA53GV2H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.11546&json=true","fetch_graph":"https://pith.science/api/pith-number/DA53GV2HPBKSMAO2SPL3HGFB6M/graph.json","fetch_events":"https://pith.science/api/pith-number/DA53GV2HPBKSMAO2SPL3HGFB6M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M/action/storage_attestation","attest_author":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M/action/author_attestation","sign_citation":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M/action/citation_signature","submit_replication":"https://pith.science/pith/DA53GV2HPBKSMAO2SPL3HGFB6M/action/replication_record"}},"created_at":"2026-07-05T11:21:06.460814+00:00","updated_at":"2026-07-05T11:21:06.460814+00:00"}