{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SZVOOYAJBR5GPI64H4BZD2ULXZ","short_pith_number":"pith:SZVOOYAJ","schema_version":"1.0","canonical_sha256":"966ae760090c7a67a3dc3f0391ea8bbe7f0371ce4585f5a146751ffe7d172c6c","source":{"kind":"arxiv","id":"2304.03412","version":2},"attestation_state":"computed","paper":{"title":"One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Abhinau K. Venkataramanan, Alan C. Bovik, Cosmin Stejerean, Ioannis Katsavounidis","submitted_at":"2023-04-06T23:26:54Z","abstract_excerpt":"The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation shar"},"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":"2304.03412","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-04-06T23:26:54Z","cross_cats_sorted":[],"title_canon_sha256":"4518e682025153241d471dbfee1572eaa6ee72975fe810e5fd2b8a885383ae93","abstract_canon_sha256":"839c5ec4ae9cc771193b84d33c1c479b6292aa59cb035d1b57c201e6b2662a0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:14:10.915604Z","signature_b64":"ZJbEW9TT+k+y3MnRndpxgjO4czkgUVTwmQDmdvgpubYvFWP2U2CTBEHmIdKx6y19a+0KnLUF4tjVEgsMxPiMAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"966ae760090c7a67a3dc3f0391ea8bbe7f0371ce4585f5a146751ffe7d172c6c","last_reissued_at":"2026-07-05T07:14:10.915129Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:14:10.915129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Abhinau K. Venkataramanan, Alan C. Bovik, Cosmin Stejerean, Ioannis Katsavounidis","submitted_at":"2023-04-06T23:26:54Z","abstract_excerpt":"The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF requires the evaluation of a heterogeneous set of quality models, it is computationally expensive. Given other advances in hardware-accelerated encoding, quality assessment is emerging as a significant bottleneck in video compression pipelines. Towards alleviating this burden, we propose a novel Fusion of Unified Quality Evaluators (FUNQUE) framework, by enabling computation shar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.03412","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/2304.03412/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":"2304.03412","created_at":"2026-07-05T07:14:10.915181+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.03412v2","created_at":"2026-07-05T07:14:10.915181+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.03412","created_at":"2026-07-05T07:14:10.915181+00:00"},{"alias_kind":"pith_short_12","alias_value":"SZVOOYAJBR5G","created_at":"2026-07-05T07:14:10.915181+00:00"},{"alias_kind":"pith_short_16","alias_value":"SZVOOYAJBR5GPI64","created_at":"2026-07-05T07:14:10.915181+00:00"},{"alias_kind":"pith_short_8","alias_value":"SZVOOYAJ","created_at":"2026-07-05T07:14:10.915181+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/SZVOOYAJBR5GPI64H4BZD2ULXZ","json":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ.json","graph_json":"https://pith.science/api/pith-number/SZVOOYAJBR5GPI64H4BZD2ULXZ/graph.json","events_json":"https://pith.science/api/pith-number/SZVOOYAJBR5GPI64H4BZD2ULXZ/events.json","paper":"https://pith.science/paper/SZVOOYAJ"},"agent_actions":{"view_html":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ","download_json":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ.json","view_paper":"https://pith.science/paper/SZVOOYAJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.03412&json=true","fetch_graph":"https://pith.science/api/pith-number/SZVOOYAJBR5GPI64H4BZD2ULXZ/graph.json","fetch_events":"https://pith.science/api/pith-number/SZVOOYAJBR5GPI64H4BZD2ULXZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ/action/storage_attestation","attest_author":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ/action/author_attestation","sign_citation":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ/action/citation_signature","submit_replication":"https://pith.science/pith/SZVOOYAJBR5GPI64H4BZD2ULXZ/action/replication_record"}},"created_at":"2026-07-05T07:14:10.915181+00:00","updated_at":"2026-07-05T07:14:10.915181+00:00"}