{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NP7JWYM6LAMPGCK7KSOZN6XDCA","short_pith_number":"pith:NP7JWYM6","canonical_record":{"source":{"id":"1906.10057","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-24T16:26:03Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"02b3e480b991c9f55c5c62d04877d19c4870d21eefcd6dcbee44b9915df20dab","abstract_canon_sha256":"9a8ff5430c89676c0025cf4d6a03a41b07e7da7e71e0c6f47f9764e30724a997"},"schema_version":"1.0"},"canonical_sha256":"6bfe9b619e5818f3095f549d96fae3103ece886e2f3e6b457e1cd56c4059f579","source":{"kind":"arxiv","id":"1906.10057","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.10057","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"arxiv_version","alias_value":"1906.10057v2","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.10057","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"pith_short_12","alias_value":"NP7JWYM6LAMP","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"pith_short_16","alias_value":"NP7JWYM6LAMPGCK7","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"pith_short_8","alias_value":"NP7JWYM6","created_at":"2026-07-05T01:12:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NP7JWYM6LAMPGCK7KSOZN6XDCA","target":"record","payload":{"canonical_record":{"source":{"id":"1906.10057","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-24T16:26:03Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"02b3e480b991c9f55c5c62d04877d19c4870d21eefcd6dcbee44b9915df20dab","abstract_canon_sha256":"9a8ff5430c89676c0025cf4d6a03a41b07e7da7e71e0c6f47f9764e30724a997"},"schema_version":"1.0"},"canonical_sha256":"6bfe9b619e5818f3095f549d96fae3103ece886e2f3e6b457e1cd56c4059f579","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:12:20.420325Z","signature_b64":"JiE22ZA3fmbyE2kN38Jhuj7BU8U1MOMJM5zQqfaA/IfQ1ba2g+UYTcSGxPWa7p18EmOFwDwzaUjdd1xHG38XDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6bfe9b619e5818f3095f549d96fae3103ece886e2f3e6b457e1cd56c4059f579","last_reissued_at":"2026-07-05T01:12:20.419917Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:12:20.419917Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.10057","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:12:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GDZzEUf6+rllhKPu+XIQ1vIirBu//F+lM9FB4/i4BZ445BxBJnnXb34+6BFqNzwM95CEfvtoJmo1lP8xjOabAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:48:44.737380Z"},"content_sha256":"81faf006765086a40d7bbe410faa33759a62eaefee61371a24751ba91cbf0ccf","schema_version":"1.0","event_id":"sha256:81faf006765086a40d7bbe410faa33759a62eaefee61371a24751ba91cbf0ccf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NP7JWYM6LAMPGCK7KSOZN6XDCA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"David Zhang, Jane You, Kede Ma, Mu Li, Wangmeng Zuo","submitted_at":"2019-06-24T16:26:03Z","abstract_excerpt":"Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized image compression, the latent codes are usually assumed to be fully statistically factorized in order to simplify entropy modeling. However, this assumption generally does not hold true and may hinder compression performance. Here we present context-based convolutional networks (CCNs) for efficient and effective entropy modeling. In particular, a 3D zigzag scanning order and a 3D code dividing technique are introduced to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.10057","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/1906.10057/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:12:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E5vh6JlFC/s2qTtPNdRReOeZzpHTQyV2x8FTqCt/SAWLec6knu3GiJTJmaYZTUwnZoCrNGqmgkV8d1+nfJYMAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T18:48:44.738090Z"},"content_sha256":"0be85556f753c320cf021f59597ee2eeaac66e61ec2b622b8072ac1908ac2488","schema_version":"1.0","event_id":"sha256:0be85556f753c320cf021f59597ee2eeaac66e61ec2b622b8072ac1908ac2488"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NP7JWYM6LAMPGCK7KSOZN6XDCA/bundle.json","state_url":"https://pith.science/pith/NP7JWYM6LAMPGCK7KSOZN6XDCA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NP7JWYM6LAMPGCK7KSOZN6XDCA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-10T18:48:44Z","links":{"resolver":"https://pith.science/pith/NP7JWYM6LAMPGCK7KSOZN6XDCA","bundle":"https://pith.science/pith/NP7JWYM6LAMPGCK7KSOZN6XDCA/bundle.json","state":"https://pith.science/pith/NP7JWYM6LAMPGCK7KSOZN6XDCA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NP7JWYM6LAMPGCK7KSOZN6XDCA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NP7JWYM6LAMPGCK7KSOZN6XDCA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9a8ff5430c89676c0025cf4d6a03a41b07e7da7e71e0c6f47f9764e30724a997","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-24T16:26:03Z","title_canon_sha256":"02b3e480b991c9f55c5c62d04877d19c4870d21eefcd6dcbee44b9915df20dab"},"schema_version":"1.0","source":{"id":"1906.10057","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.10057","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"arxiv_version","alias_value":"1906.10057v2","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.10057","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"pith_short_12","alias_value":"NP7JWYM6LAMP","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"pith_short_16","alias_value":"NP7JWYM6LAMPGCK7","created_at":"2026-07-05T01:12:20Z"},{"alias_kind":"pith_short_8","alias_value":"NP7JWYM6","created_at":"2026-07-05T01:12:20Z"}],"graph_snapshots":[{"event_id":"sha256:0be85556f753c320cf021f59597ee2eeaac66e61ec2b622b8072ac1908ac2488","target":"graph","created_at":"2026-07-05T01:12:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1906.10057/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized image compression, the latent codes are usually assumed to be fully statistically factorized in order to simplify entropy modeling. However, this assumption generally does not hold true and may hinder compression performance. Here we present context-based convolutional networks (CCNs) for efficient and effective entropy modeling. In particular, a 3D zigzag scanning order and a 3D code dividing technique are introduced to","authors_text":"David Zhang, Jane You, Kede Ma, Mu Li, Wangmeng Zuo","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-24T16:26:03Z","title":"Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.10057","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:81faf006765086a40d7bbe410faa33759a62eaefee61371a24751ba91cbf0ccf","target":"record","created_at":"2026-07-05T01:12:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9a8ff5430c89676c0025cf4d6a03a41b07e7da7e71e0c6f47f9764e30724a997","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-24T16:26:03Z","title_canon_sha256":"02b3e480b991c9f55c5c62d04877d19c4870d21eefcd6dcbee44b9915df20dab"},"schema_version":"1.0","source":{"id":"1906.10057","kind":"arxiv","version":2}},"canonical_sha256":"6bfe9b619e5818f3095f549d96fae3103ece886e2f3e6b457e1cd56c4059f579","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6bfe9b619e5818f3095f549d96fae3103ece886e2f3e6b457e1cd56c4059f579","first_computed_at":"2026-07-05T01:12:20.419917Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:12:20.419917Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JiE22ZA3fmbyE2kN38Jhuj7BU8U1MOMJM5zQqfaA/IfQ1ba2g+UYTcSGxPWa7p18EmOFwDwzaUjdd1xHG38XDg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:12:20.420325Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.10057","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:81faf006765086a40d7bbe410faa33759a62eaefee61371a24751ba91cbf0ccf","sha256:0be85556f753c320cf021f59597ee2eeaac66e61ec2b622b8072ac1908ac2488"],"state_sha256":"39ce2839a8b09588bcf648e107360c3f43bde5c5174526507bcb0022c764a86b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AtEnjqtwc8bC96rS5J1L8U0Ccc1Uv2hIuzVeww9nKqmjcWUMMzz71i2V5dHN9XEFj0Y/JFqHXe7rUyn3MV+AAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T18:48:44.742797Z","bundle_sha256":"784b444a8b81b3bcd6e9cdf84c55aada140d4e09c18f2f4e36a202bfe77bd342"}}