{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:OJNSMZW3IU7BRSLSPHTU2WYCV5","short_pith_number":"pith:OJNSMZW3","schema_version":"1.0","canonical_sha256":"725b2666db453e18c97279e74d5b02af6db5969896eb883efec9ad073aab981b","source":{"kind":"arxiv","id":"1710.01218","version":3},"attestation_state":"computed","paper":{"title":"Reducing Complexity of HEVC: A Deep Learning Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mai Xu, Ren Yang, Tianyi Li, Xin Deng, Zhenyu Guan, Zulin Wang","submitted_at":"2017-09-19T02:02:00Z","abstract_excerpt":"High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the proceeding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the bruteforce search for rate-distortion optimization (RDO). Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network. First,"},"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":"1710.01218","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-09-19T02:02:00Z","cross_cats_sorted":[],"title_canon_sha256":"01d02e1ff38b440be75ebc4f6b78b91401329141bd0f9956e2ec033e26e4e35f","abstract_canon_sha256":"420cb8408de5b695f26a613097cf7178b1ece4786a4a9a0fbc93a230519601d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:08.338366Z","signature_b64":"Xh6rmy7OPFwktb+G9F4/JifEf4Y6tpMAPlX5ct+4q4btgEzUlsx5VJrEfRGkuQkOd/z6B7xF5uZdieTZJ4dYDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"725b2666db453e18c97279e74d5b02af6db5969896eb883efec9ad073aab981b","last_reissued_at":"2026-05-17T23:52:08.337866Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:08.337866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reducing Complexity of HEVC: A Deep Learning Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mai Xu, Ren Yang, Tianyi Li, Xin Deng, Zhenyu Guan, Zulin Wang","submitted_at":"2017-09-19T02:02:00Z","abstract_excerpt":"High Efficiency Video Coding (HEVC) significantly reduces bit-rates over the proceeding H.264 standard but at the expense of extremely high encoding complexity. In HEVC, the quad-tree partition of coding unit (CU) consumes a large proportion of the HEVC encoding complexity, due to the bruteforce search for rate-distortion optimization (RDO). Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network. First,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.01218","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1710.01218","created_at":"2026-05-17T23:52:08.337953+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.01218v3","created_at":"2026-05-17T23:52:08.337953+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.01218","created_at":"2026-05-17T23:52:08.337953+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJNSMZW3IU7B","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJNSMZW3IU7BRSLS","created_at":"2026-05-18T12:31:34.259226+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJNSMZW3","created_at":"2026-05-18T12:31:34.259226+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/OJNSMZW3IU7BRSLSPHTU2WYCV5","json":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5.json","graph_json":"https://pith.science/api/pith-number/OJNSMZW3IU7BRSLSPHTU2WYCV5/graph.json","events_json":"https://pith.science/api/pith-number/OJNSMZW3IU7BRSLSPHTU2WYCV5/events.json","paper":"https://pith.science/paper/OJNSMZW3"},"agent_actions":{"view_html":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5","download_json":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5.json","view_paper":"https://pith.science/paper/OJNSMZW3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.01218&json=true","fetch_graph":"https://pith.science/api/pith-number/OJNSMZW3IU7BRSLSPHTU2WYCV5/graph.json","fetch_events":"https://pith.science/api/pith-number/OJNSMZW3IU7BRSLSPHTU2WYCV5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5/action/storage_attestation","attest_author":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5/action/author_attestation","sign_citation":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5/action/citation_signature","submit_replication":"https://pith.science/pith/OJNSMZW3IU7BRSLSPHTU2WYCV5/action/replication_record"}},"created_at":"2026-05-17T23:52:08.337953+00:00","updated_at":"2026-05-17T23:52:08.337953+00:00"}