{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:L3GBFAX72EMFZSV6GIBPZ6TPSQ","short_pith_number":"pith:L3GBFAX7","schema_version":"1.0","canonical_sha256":"5ecc1282ffd1185ccabe3202fcfa6f94340126797fe8cb44395416f9a7429709","source":{"kind":"arxiv","id":"1807.03470","version":1},"attestation_state":"computed","paper":{"title":"Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianrui Cai, Lei Zhang, Zisheng Cao","submitted_at":"2018-07-10T03:40:36Z","abstract_excerpt":"Lossy image compression (LIC), which aims to utilize inexact approximations to represent an image more compactly, is a classical problem in image processing. Recently, deep convolutional neural networks (CNNs) have achieved interesting results in LIC by learning an encoder-quantizer-decoder network from a large amount of data. However, existing CNN-based LIC methods usually can only train a network for a specific bits-per-pixel (bpp). Such a \"one network per bpp\" problem limits the generality and flexibility of CNNs to practical LIC applications. In this paper, we propose to learn a single CNN"},"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":"1807.03470","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-10T03:40:36Z","cross_cats_sorted":[],"title_canon_sha256":"7718b48227aa7ea57f35a514748ce583d0ed8ed794b5ad89c2b23056caf6f670","abstract_canon_sha256":"1bfe9dcfa442cffc5c4b5761a3752b10cacba97396017a09169956eea5e7e060"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:10.352796Z","signature_b64":"KxEP2DxObFe9t1UxzGSOW7ggSyrso+MuB80n30wIrLG7GiZjIFh0rk/wiRidJ8t27aVTBbHzVcghiVkCnqBtDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ecc1282ffd1185ccabe3202fcfa6f94340126797fe8cb44395416f9a7429709","last_reissued_at":"2026-05-18T00:11:10.352069Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:10.352069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jianrui Cai, Lei Zhang, Zisheng Cao","submitted_at":"2018-07-10T03:40:36Z","abstract_excerpt":"Lossy image compression (LIC), which aims to utilize inexact approximations to represent an image more compactly, is a classical problem in image processing. Recently, deep convolutional neural networks (CNNs) have achieved interesting results in LIC by learning an encoder-quantizer-decoder network from a large amount of data. However, existing CNN-based LIC methods usually can only train a network for a specific bits-per-pixel (bpp). Such a \"one network per bpp\" problem limits the generality and flexibility of CNNs to practical LIC applications. In this paper, we propose to learn a single CNN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.03470","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":""},"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":"1807.03470","created_at":"2026-05-18T00:11:10.352193+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.03470v1","created_at":"2026-05-18T00:11:10.352193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.03470","created_at":"2026-05-18T00:11:10.352193+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3GBFAX72EMF","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3GBFAX72EMFZSV6","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3GBFAX7","created_at":"2026-05-18T12:32:33.847187+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/L3GBFAX72EMFZSV6GIBPZ6TPSQ","json":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ.json","graph_json":"https://pith.science/api/pith-number/L3GBFAX72EMFZSV6GIBPZ6TPSQ/graph.json","events_json":"https://pith.science/api/pith-number/L3GBFAX72EMFZSV6GIBPZ6TPSQ/events.json","paper":"https://pith.science/paper/L3GBFAX7"},"agent_actions":{"view_html":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ","download_json":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ.json","view_paper":"https://pith.science/paper/L3GBFAX7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.03470&json=true","fetch_graph":"https://pith.science/api/pith-number/L3GBFAX72EMFZSV6GIBPZ6TPSQ/graph.json","fetch_events":"https://pith.science/api/pith-number/L3GBFAX72EMFZSV6GIBPZ6TPSQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ/action/storage_attestation","attest_author":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ/action/author_attestation","sign_citation":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ/action/citation_signature","submit_replication":"https://pith.science/pith/L3GBFAX72EMFZSV6GIBPZ6TPSQ/action/replication_record"}},"created_at":"2026-05-18T00:11:10.352193+00:00","updated_at":"2026-05-18T00:11:10.352193+00:00"}