{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IJVQWFWM4FQRXFQJHO7CO2VTTU","short_pith_number":"pith:IJVQWFWM","schema_version":"1.0","canonical_sha256":"426b0b16cce1611b96093bbe276ab39d2318b288d81da2b16d771cb8c64872b2","source":{"kind":"arxiv","id":"2109.11913","version":1},"attestation_state":"computed","paper":{"title":"Spatial Information Refinement for Chroma Intra Prediction in Video Coding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Chengyi Zou, Luis Herranz, Marc Gorriz Blanch, Marta Mrak, Shuai Wan, Tiannan Ji","submitted_at":"2021-09-24T12:07:49Z","abstract_excerpt":"Video compression benefits from advanced chroma intra prediction methods, such as the Cross-Component Linear Model (CCLM) which uses linear models to approximate the relationship between the luma and chroma components. Recently it has been proven that advanced cross-component prediction methods based on Neural Networks (NN) can bring additional coding gains. In this paper, spatial information refinement is proposed for improving NN-based chroma intra prediction. Specifically, the performance of chroma intra prediction can be improved by refined down-sampling or by incorporating location inform"},"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":"2109.11913","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2021-09-24T12:07:49Z","cross_cats_sorted":[],"title_canon_sha256":"1cb3a140af1c1d23eebb25ac4d233e122823e980eeb6661d6f36180e375942e7","abstract_canon_sha256":"be1280805eb716c5062600a43da6188f163b31801d85ee8529e1a581cacb533d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:17:08.812462Z","signature_b64":"ZmAA8lz1Hq2VdqLZFTMbxT9Tqiwgfg0ecw7WghS0fXC48eQT5r4aszH6YOCAbAhrpvvFWempcOoeLCHMWFrMAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"426b0b16cce1611b96093bbe276ab39d2318b288d81da2b16d771cb8c64872b2","last_reissued_at":"2026-07-05T03:17:08.811995Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:17:08.811995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatial Information Refinement for Chroma Intra Prediction in Video Coding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Chengyi Zou, Luis Herranz, Marc Gorriz Blanch, Marta Mrak, Shuai Wan, Tiannan Ji","submitted_at":"2021-09-24T12:07:49Z","abstract_excerpt":"Video compression benefits from advanced chroma intra prediction methods, such as the Cross-Component Linear Model (CCLM) which uses linear models to approximate the relationship between the luma and chroma components. Recently it has been proven that advanced cross-component prediction methods based on Neural Networks (NN) can bring additional coding gains. In this paper, spatial information refinement is proposed for improving NN-based chroma intra prediction. Specifically, the performance of chroma intra prediction can be improved by refined down-sampling or by incorporating location inform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.11913","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/2109.11913/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":"2109.11913","created_at":"2026-07-05T03:17:08.812065+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.11913v1","created_at":"2026-07-05T03:17:08.812065+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.11913","created_at":"2026-07-05T03:17:08.812065+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJVQWFWM4FQR","created_at":"2026-07-05T03:17:08.812065+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJVQWFWM4FQRXFQJ","created_at":"2026-07-05T03:17:08.812065+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJVQWFWM","created_at":"2026-07-05T03:17:08.812065+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/IJVQWFWM4FQRXFQJHO7CO2VTTU","json":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU.json","graph_json":"https://pith.science/api/pith-number/IJVQWFWM4FQRXFQJHO7CO2VTTU/graph.json","events_json":"https://pith.science/api/pith-number/IJVQWFWM4FQRXFQJHO7CO2VTTU/events.json","paper":"https://pith.science/paper/IJVQWFWM"},"agent_actions":{"view_html":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU","download_json":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU.json","view_paper":"https://pith.science/paper/IJVQWFWM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.11913&json=true","fetch_graph":"https://pith.science/api/pith-number/IJVQWFWM4FQRXFQJHO7CO2VTTU/graph.json","fetch_events":"https://pith.science/api/pith-number/IJVQWFWM4FQRXFQJHO7CO2VTTU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU/action/storage_attestation","attest_author":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU/action/author_attestation","sign_citation":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU/action/citation_signature","submit_replication":"https://pith.science/pith/IJVQWFWM4FQRXFQJHO7CO2VTTU/action/replication_record"}},"created_at":"2026-07-05T03:17:08.812065+00:00","updated_at":"2026-07-05T03:17:08.812065+00:00"}