{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Y3OMVJUGHYLFEM2UYS4SAXLJ4T","short_pith_number":"pith:Y3OMVJUG","schema_version":"1.0","canonical_sha256":"c6dccaa6863e16523354c4b9205d69e4c948b5e4ac5ccf92f8fabb16ad258dd8","source":{"kind":"arxiv","id":"2411.09308","version":1},"attestation_state":"computed","paper":{"title":"DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Junqi Liu, Sam Kwong, Xiaoqi Wang, Xu Long, Yun Zhang","submitted_at":"2024-11-14T09:34:36Z","abstract_excerpt":"Just Recognizable Difference (JRD) represents the minimum visual difference that is detectable by machine vision, which can be exploited to promote machine vision oriented visual signal processing. In this paper, we propose a Deep Transformer based JRD (DT-JRD) prediction model for Video Coding for Machines (VCM), where the accurately predicted JRD can be used reduce the coding bit rate while maintaining the accuracy of machine tasks. Firstly, we model the JRD prediction as a multi-class classification and propose a DT-JRD prediction model that integrates an improved embedding, a content and d"},"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":"2411.09308","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-14T09:34:36Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"148ece1ff96000bde67ac006782540b2c8e2ac8ddd199098e98ba8c509bcaaef","abstract_canon_sha256":"40d8a5712ca39e4d0305d289e119fa754905e6c41e468f69b6f03e5035e82ce1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:24.832113Z","signature_b64":"npqtiIX/J9l2lBvFFUfwgwGs/Bjtg8mIEa46GyTU6N9BaEcC80Bh4XVVregoPvHHAo0cVVUe9/A67H0GKkxRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6dccaa6863e16523354c4b9205d69e4c948b5e4ac5ccf92f8fabb16ad258dd8","last_reissued_at":"2026-07-05T09:35:24.831613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:24.831613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Junqi Liu, Sam Kwong, Xiaoqi Wang, Xu Long, Yun Zhang","submitted_at":"2024-11-14T09:34:36Z","abstract_excerpt":"Just Recognizable Difference (JRD) represents the minimum visual difference that is detectable by machine vision, which can be exploited to promote machine vision oriented visual signal processing. In this paper, we propose a Deep Transformer based JRD (DT-JRD) prediction model for Video Coding for Machines (VCM), where the accurately predicted JRD can be used reduce the coding bit rate while maintaining the accuracy of machine tasks. Firstly, we model the JRD prediction as a multi-class classification and propose a DT-JRD prediction model that integrates an improved embedding, a content and d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09308","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/2411.09308/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":"2411.09308","created_at":"2026-07-05T09:35:24.831683+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.09308v1","created_at":"2026-07-05T09:35:24.831683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09308","created_at":"2026-07-05T09:35:24.831683+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y3OMVJUGHYLF","created_at":"2026-07-05T09:35:24.831683+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y3OMVJUGHYLFEM2U","created_at":"2026-07-05T09:35:24.831683+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y3OMVJUG","created_at":"2026-07-05T09:35:24.831683+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/Y3OMVJUGHYLFEM2UYS4SAXLJ4T","json":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T.json","graph_json":"https://pith.science/api/pith-number/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/graph.json","events_json":"https://pith.science/api/pith-number/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/events.json","paper":"https://pith.science/paper/Y3OMVJUG"},"agent_actions":{"view_html":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T","download_json":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T.json","view_paper":"https://pith.science/paper/Y3OMVJUG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.09308&json=true","fetch_graph":"https://pith.science/api/pith-number/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/graph.json","fetch_events":"https://pith.science/api/pith-number/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/action/storage_attestation","attest_author":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/action/author_attestation","sign_citation":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/action/citation_signature","submit_replication":"https://pith.science/pith/Y3OMVJUGHYLFEM2UYS4SAXLJ4T/action/replication_record"}},"created_at":"2026-07-05T09:35:24.831683+00:00","updated_at":"2026-07-05T09:35:24.831683+00:00"}