{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:A65SXFLQGTSR43AO3YY6DYCFDP","short_pith_number":"pith:A65SXFLQ","schema_version":"1.0","canonical_sha256":"07bb2b957034e51e6c0ede31e1e0451bdd1d63fa14f764a6137412c2eea01f96","source":{"kind":"arxiv","id":"2407.12295","version":1},"attestation_state":"computed","paper":{"title":"Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Junhui Li, Xingsong Hou","submitted_at":"2024-07-17T03:33:16Z","abstract_excerpt":"Deep learning-based methods have garnered significant attention in remote sensing (RS) image compression due to their superior performance. Most of these methods focus on enhancing the coding capability of the compression network and improving entropy model prediction accuracy. However, they typically compress and decompress each image independently, ignoring the significant inter-image similarity prior. In this paper, we propose a codebook-based RS image compression (Code-RSIC) method with a generated discrete codebook, which is deployed at the decoding end of a compression algorithm to provi"},"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":"2407.12295","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-17T03:33:16Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"38aa3c7598709174493903451c058895b8907be6071930a5794449841274fa0b","abstract_canon_sha256":"853f3ffedc4750eb9a2a8da038097839a787bcc1c4820582b4d4da9d1ad764a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:45:02.619984Z","signature_b64":"q26WFPUPpFH3DgtVZZDTg0oswrvMq3Ij/QPQHZklIXvpNxYvDNmBHcoEvalNB+zm1GP994uNhLQ6TQ0MvsX5Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07bb2b957034e51e6c0ede31e1e0451bdd1d63fa14f764a6137412c2eea01f96","last_reissued_at":"2026-07-05T08:45:02.619591Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:45:02.619591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Junhui Li, Xingsong Hou","submitted_at":"2024-07-17T03:33:16Z","abstract_excerpt":"Deep learning-based methods have garnered significant attention in remote sensing (RS) image compression due to their superior performance. Most of these methods focus on enhancing the coding capability of the compression network and improving entropy model prediction accuracy. However, they typically compress and decompress each image independently, ignoring the significant inter-image similarity prior. In this paper, we propose a codebook-based RS image compression (Code-RSIC) method with a generated discrete codebook, which is deployed at the decoding end of a compression algorithm to provi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.12295","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/2407.12295/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":"2407.12295","created_at":"2026-07-05T08:45:02.619649+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.12295v1","created_at":"2026-07-05T08:45:02.619649+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.12295","created_at":"2026-07-05T08:45:02.619649+00:00"},{"alias_kind":"pith_short_12","alias_value":"A65SXFLQGTSR","created_at":"2026-07-05T08:45:02.619649+00:00"},{"alias_kind":"pith_short_16","alias_value":"A65SXFLQGTSR43AO","created_at":"2026-07-05T08:45:02.619649+00:00"},{"alias_kind":"pith_short_8","alias_value":"A65SXFLQ","created_at":"2026-07-05T08:45:02.619649+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.10650","citing_title":"Deep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability,Throughput, and Latency","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP","json":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP.json","graph_json":"https://pith.science/api/pith-number/A65SXFLQGTSR43AO3YY6DYCFDP/graph.json","events_json":"https://pith.science/api/pith-number/A65SXFLQGTSR43AO3YY6DYCFDP/events.json","paper":"https://pith.science/paper/A65SXFLQ"},"agent_actions":{"view_html":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP","download_json":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP.json","view_paper":"https://pith.science/paper/A65SXFLQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.12295&json=true","fetch_graph":"https://pith.science/api/pith-number/A65SXFLQGTSR43AO3YY6DYCFDP/graph.json","fetch_events":"https://pith.science/api/pith-number/A65SXFLQGTSR43AO3YY6DYCFDP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP/action/storage_attestation","attest_author":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP/action/author_attestation","sign_citation":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP/action/citation_signature","submit_replication":"https://pith.science/pith/A65SXFLQGTSR43AO3YY6DYCFDP/action/replication_record"}},"created_at":"2026-07-05T08:45:02.619649+00:00","updated_at":"2026-07-05T08:45:02.619649+00:00"}