{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Q2R6L6BP4NHU4T24A5ECBZ6FYC","short_pith_number":"pith:Q2R6L6BP","schema_version":"1.0","canonical_sha256":"86a3e5f82fe34f4e4f5c074820e7c5c0aa05db1fc6fd6556ca9672fdbe9237b2","source":{"kind":"arxiv","id":"2112.06334","version":2},"attestation_state":"computed","paper":{"title":"DPICT: Deep Progressive Image Compression Using Trit-Planes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chang-Su Kim, Jae-Han Lee, Kwang Pyo Choi, Seungmin Jeon, Youngo Park","submitted_at":"2021-12-12T22:09:33Z","abstract_excerpt":"We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS). First, we transform an image into a latent tensor using an analysis network. Then, we represent the latent tensor in ternary digits (trits) and encode it into a compressed bitstream trit-plane by trit-plane in the decreasing order of significance. Moreover, within each trit-plane, we sort the trits according to their rate-distortion priorities and transmit more important information first. Since the compression network is les"},"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":"2112.06334","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-12-12T22:09:33Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a4f1b1297702696e12edcc438dcd9535085914048270ad17a4ea01851206aa7d","abstract_canon_sha256":"2b3c0096eab1e78407b7c759f6f4a702687b9c08425d7929337dcf055a1dc8bc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:44.015401Z","signature_b64":"eEHwkezminYgNDUqwvZm5simzcGCyovJY4xVG3jqwm2c1IFjgtkntLDJVRcS0ZOhD/LaPF/4K0pZKyV/jn8IDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86a3e5f82fe34f4e4f5c074820e7c5c0aa05db1fc6fd6556ca9672fdbe9237b2","last_reissued_at":"2026-07-05T04:20:44.014990Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:44.014990Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DPICT: Deep Progressive Image Compression Using Trit-Planes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chang-Su Kim, Jae-Han Lee, Kwang Pyo Choi, Seungmin Jeon, Youngo Park","submitted_at":"2021-12-12T22:09:33Z","abstract_excerpt":"We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS). First, we transform an image into a latent tensor using an analysis network. Then, we represent the latent tensor in ternary digits (trits) and encode it into a compressed bitstream trit-plane by trit-plane in the decreasing order of significance. Moreover, within each trit-plane, we sort the trits according to their rate-distortion priorities and transmit more important information first. Since the compression network is les"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.06334","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/2112.06334/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":"2112.06334","created_at":"2026-07-05T04:20:44.015056+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.06334v2","created_at":"2026-07-05T04:20:44.015056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.06334","created_at":"2026-07-05T04:20:44.015056+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q2R6L6BP4NHU","created_at":"2026-07-05T04:20:44.015056+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q2R6L6BP4NHU4T24","created_at":"2026-07-05T04:20:44.015056+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q2R6L6BP","created_at":"2026-07-05T04:20:44.015056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.00687","citing_title":"Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC","json":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC.json","graph_json":"https://pith.science/api/pith-number/Q2R6L6BP4NHU4T24A5ECBZ6FYC/graph.json","events_json":"https://pith.science/api/pith-number/Q2R6L6BP4NHU4T24A5ECBZ6FYC/events.json","paper":"https://pith.science/paper/Q2R6L6BP"},"agent_actions":{"view_html":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC","download_json":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC.json","view_paper":"https://pith.science/paper/Q2R6L6BP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.06334&json=true","fetch_graph":"https://pith.science/api/pith-number/Q2R6L6BP4NHU4T24A5ECBZ6FYC/graph.json","fetch_events":"https://pith.science/api/pith-number/Q2R6L6BP4NHU4T24A5ECBZ6FYC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC/action/storage_attestation","attest_author":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC/action/author_attestation","sign_citation":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC/action/citation_signature","submit_replication":"https://pith.science/pith/Q2R6L6BP4NHU4T24A5ECBZ6FYC/action/replication_record"}},"created_at":"2026-07-05T04:20:44.015056+00:00","updated_at":"2026-07-05T04:20:44.015056+00:00"}