{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VAWTO45SM7LGVGPSL2ZDP5PXIK","short_pith_number":"pith:VAWTO45S","schema_version":"1.0","canonical_sha256":"a82d3773b267d66a99f25eb237f5f742b850d148397db9d76b05b588bd265c00","source":{"kind":"arxiv","id":"2307.08265","version":3},"attestation_state":"computed","paper":{"title":"Extreme Image Compression using Fine-tuned VQGANs","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Meng Wang, Qi Mao, Shiqi Wang, Siwei Ma, Tinghan Yang, Yinuo Zhang, Zijian Wang","submitted_at":"2023-07-17T06:14:19Z","abstract_excerpt":"Recent advances in generative compression methods have demonstrated remarkable progress in enhancing the perceptual quality of compressed data, especially in scenarios with low bitrates. However, their efficacy and applicability to achieve extreme compression ratios ($<0.05$ bpp) remain constrained. In this work, we propose a simple yet effective coding framework by introducing vector quantization (VQ)--based generative models into the image compression domain. The main insight is that the codebook learned by the VQGAN model yields a strong expressive capacity, facilitating efficient compressi"},"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":"2307.08265","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-17T06:14:19Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"be71591fc03c45cfc97d495f4410d67ce64c213892416b5afd8b2640458c41ca","abstract_canon_sha256":"910b9b0185d10fb568f7750937ff4d96bb61406d4175ba1ae139eb0b337b41d2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:37.492826Z","signature_b64":"IXlEJog5t81OlT1Ggd73CIPg1XsfpC9sOlDB/jPg/IE2LaTUvQL/MkI9XBBDFVwLdMPMtLIQyxMGr1nONwXXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a82d3773b267d66a99f25eb237f5f742b850d148397db9d76b05b588bd265c00","last_reissued_at":"2026-07-05T07:24:37.492300Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:37.492300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extreme Image Compression using Fine-tuned VQGANs","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Meng Wang, Qi Mao, Shiqi Wang, Siwei Ma, Tinghan Yang, Yinuo Zhang, Zijian Wang","submitted_at":"2023-07-17T06:14:19Z","abstract_excerpt":"Recent advances in generative compression methods have demonstrated remarkable progress in enhancing the perceptual quality of compressed data, especially in scenarios with low bitrates. However, their efficacy and applicability to achieve extreme compression ratios ($<0.05$ bpp) remain constrained. In this work, we propose a simple yet effective coding framework by introducing vector quantization (VQ)--based generative models into the image compression domain. The main insight is that the codebook learned by the VQGAN model yields a strong expressive capacity, facilitating efficient compressi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.08265","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.08265/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":"2307.08265","created_at":"2026-07-05T07:24:37.492369+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.08265v3","created_at":"2026-07-05T07:24:37.492369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.08265","created_at":"2026-07-05T07:24:37.492369+00:00"},{"alias_kind":"pith_short_12","alias_value":"VAWTO45SM7LG","created_at":"2026-07-05T07:24:37.492369+00:00"},{"alias_kind":"pith_short_16","alias_value":"VAWTO45SM7LGVGPS","created_at":"2026-07-05T07:24:37.492369+00:00"},{"alias_kind":"pith_short_8","alias_value":"VAWTO45S","created_at":"2026-07-05T07:24:37.492369+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/VAWTO45SM7LGVGPSL2ZDP5PXIK","json":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK.json","graph_json":"https://pith.science/api/pith-number/VAWTO45SM7LGVGPSL2ZDP5PXIK/graph.json","events_json":"https://pith.science/api/pith-number/VAWTO45SM7LGVGPSL2ZDP5PXIK/events.json","paper":"https://pith.science/paper/VAWTO45S"},"agent_actions":{"view_html":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK","download_json":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK.json","view_paper":"https://pith.science/paper/VAWTO45S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.08265&json=true","fetch_graph":"https://pith.science/api/pith-number/VAWTO45SM7LGVGPSL2ZDP5PXIK/graph.json","fetch_events":"https://pith.science/api/pith-number/VAWTO45SM7LGVGPSL2ZDP5PXIK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK/action/storage_attestation","attest_author":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK/action/author_attestation","sign_citation":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK/action/citation_signature","submit_replication":"https://pith.science/pith/VAWTO45SM7LGVGPSL2ZDP5PXIK/action/replication_record"}},"created_at":"2026-07-05T07:24:37.492369+00:00","updated_at":"2026-07-05T07:24:37.492369+00:00"}