{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:T3XG2J3WZTPNGSQJ2UUAGB2HLS","short_pith_number":"pith:T3XG2J3W","schema_version":"1.0","canonical_sha256":"9eee6d2776ccded34a09d5280307475cbda5172620081e8dc9dd816415330e4e","source":{"kind":"arxiv","id":"2411.16119","version":1},"attestation_state":"computed","paper":{"title":"Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Xiaolin Wu, Xi Zhang","submitted_at":"2024-11-25T06:05:08Z","abstract_excerpt":"It is customary to deploy uniform scalar quantization in the end-to-end optimized Neural image compression methods, instead of more powerful vector quantization, due to the high complexity of the latter. Lattice vector quantization (LVQ), on the other hand, presents a compelling alternative, which can exploit inter-feature dependencies more effectively while keeping computational efficiency almost the same as scalar quantization. However, traditional LVQ structures are designed/optimized for uniform source distributions, hence nonadaptive and suboptimal for real source distributions of latent "},"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":"2411.16119","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-25T06:05:08Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"365f5c8673adc4c9f345017e86137ed5a5221004adc374d52c0318ed892855c4","abstract_canon_sha256":"afefc34a08047fad0ad3e278343a5edb252a73e90fec56e165f06cda7edf69ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:04.444640Z","signature_b64":"mUAVsxoqBeiIFsARGaFMDQljlEgWXR5sH43GR7QblRB3hqgfP4d8+TNQ6JHLZT2RzJ5wcudsqhssXkIOFkHuBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9eee6d2776ccded34a09d5280307475cbda5172620081e8dc9dd816415330e4e","last_reissued_at":"2026-07-05T09:40:04.444237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:04.444237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Xiaolin Wu, Xi Zhang","submitted_at":"2024-11-25T06:05:08Z","abstract_excerpt":"It is customary to deploy uniform scalar quantization in the end-to-end optimized Neural image compression methods, instead of more powerful vector quantization, due to the high complexity of the latter. Lattice vector quantization (LVQ), on the other hand, presents a compelling alternative, which can exploit inter-feature dependencies more effectively while keeping computational efficiency almost the same as scalar quantization. However, traditional LVQ structures are designed/optimized for uniform source distributions, hence nonadaptive and suboptimal for real source distributions of latent "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16119","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/2411.16119/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":"2411.16119","created_at":"2026-07-05T09:40:04.444294+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.16119v1","created_at":"2026-07-05T09:40:04.444294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16119","created_at":"2026-07-05T09:40:04.444294+00:00"},{"alias_kind":"pith_short_12","alias_value":"T3XG2J3WZTPN","created_at":"2026-07-05T09:40:04.444294+00:00"},{"alias_kind":"pith_short_16","alias_value":"T3XG2J3WZTPNGSQJ","created_at":"2026-07-05T09:40:04.444294+00:00"},{"alias_kind":"pith_short_8","alias_value":"T3XG2J3W","created_at":"2026-07-05T09:40:04.444294+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/T3XG2J3WZTPNGSQJ2UUAGB2HLS","json":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS.json","graph_json":"https://pith.science/api/pith-number/T3XG2J3WZTPNGSQJ2UUAGB2HLS/graph.json","events_json":"https://pith.science/api/pith-number/T3XG2J3WZTPNGSQJ2UUAGB2HLS/events.json","paper":"https://pith.science/paper/T3XG2J3W"},"agent_actions":{"view_html":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS","download_json":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS.json","view_paper":"https://pith.science/paper/T3XG2J3W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.16119&json=true","fetch_graph":"https://pith.science/api/pith-number/T3XG2J3WZTPNGSQJ2UUAGB2HLS/graph.json","fetch_events":"https://pith.science/api/pith-number/T3XG2J3WZTPNGSQJ2UUAGB2HLS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS/action/storage_attestation","attest_author":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS/action/author_attestation","sign_citation":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS/action/citation_signature","submit_replication":"https://pith.science/pith/T3XG2J3WZTPNGSQJ2UUAGB2HLS/action/replication_record"}},"created_at":"2026-07-05T09:40:04.444294+00:00","updated_at":"2026-07-05T09:40:04.444294+00:00"}