{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UVHGBBUENIO35OOHJKA72NFHHM","short_pith_number":"pith:UVHGBBUE","schema_version":"1.0","canonical_sha256":"a54e6086846a1dbeb9c74a81fd34a73b3620a63abf722a7a7a9baf72ef5ba0b5","source":{"kind":"arxiv","id":"2411.19320","version":2},"attestation_state":"computed","paper":{"title":"Generalized Gaussian Model for Learned Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Dong Liu, Haotian Zhang, Li Li","submitted_at":"2024-11-28T18:51:55Z","abstract_excerpt":"In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the Gaussian mixture models, can fit the distribution of latent variables more precisely, but the corresponding complexity is higher. To balance the compression performance and complexity, we extend the Gaussian model to the generalized Gaussian family for more flexible latent distribution modeling, introduci"},"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":"2411.19320","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-28T18:51:55Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"18da9f7429dc88d70fd839fb0de1e01827ed11f311f217a9546b44e3bcf4bcb5","abstract_canon_sha256":"79ef5692938bc34359ee285d93657ddaff750a087a9b639bfb69e69415703e83"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:52:56.847913Z","signature_b64":"mK1I+p8J8v4FEJ7pUT3CmvrQetLeV2Fxkbi4BlLMGEP0LDrtpwRbuEaeCnWvZPfmw83C+NCT/CxneotGOYABAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a54e6086846a1dbeb9c74a81fd34a73b3620a63abf722a7a7a9baf72ef5ba0b5","last_reissued_at":"2026-07-05T10:52:56.847404Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:52:56.847404Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized Gaussian Model for Learned Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Dong Liu, Haotian Zhang, Li Li","submitted_at":"2024-11-28T18:51:55Z","abstract_excerpt":"In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity and effectiveness. Probabilistic models with more parameters, such as the Gaussian mixture models, can fit the distribution of latent variables more precisely, but the corresponding complexity is higher. To balance the compression performance and complexity, we extend the Gaussian model to the generalized Gaussian family for more flexible latent distribution modeling, introduci"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.19320","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/2411.19320/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":"2411.19320","created_at":"2026-07-05T10:52:56.847468+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.19320v2","created_at":"2026-07-05T10:52:56.847468+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.19320","created_at":"2026-07-05T10:52:56.847468+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVHGBBUENIO3","created_at":"2026-07-05T10:52:56.847468+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVHGBBUENIO35OOH","created_at":"2026-07-05T10:52:56.847468+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVHGBBUE","created_at":"2026-07-05T10:52:56.847468+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.14541","citing_title":"Neural Video Compression with Context Modulation","ref_index":59,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM","json":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM.json","graph_json":"https://pith.science/api/pith-number/UVHGBBUENIO35OOHJKA72NFHHM/graph.json","events_json":"https://pith.science/api/pith-number/UVHGBBUENIO35OOHJKA72NFHHM/events.json","paper":"https://pith.science/paper/UVHGBBUE"},"agent_actions":{"view_html":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM","download_json":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM.json","view_paper":"https://pith.science/paper/UVHGBBUE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.19320&json=true","fetch_graph":"https://pith.science/api/pith-number/UVHGBBUENIO35OOHJKA72NFHHM/graph.json","fetch_events":"https://pith.science/api/pith-number/UVHGBBUENIO35OOHJKA72NFHHM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM/action/storage_attestation","attest_author":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM/action/author_attestation","sign_citation":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM/action/citation_signature","submit_replication":"https://pith.science/pith/UVHGBBUENIO35OOHJKA72NFHHM/action/replication_record"}},"created_at":"2026-07-05T10:52:56.847468+00:00","updated_at":"2026-07-05T10:52:56.847468+00:00"}