{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BCIJL5LIDCM6RPB3BXJSLYL6BZ","short_pith_number":"pith:BCIJL5LI","canonical_record":{"source":{"id":"1904.00553","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2019-04-01T04:12:57Z","cross_cats_sorted":[],"title_canon_sha256":"4cb2745dee86636ec0f8041c322f7290cfbac5f67d4f8b39dbd0374ff2a3a028","abstract_canon_sha256":"cddc50640033ee649db0f828bd2681ac1004ae5172313395acace060caafd15d"},"schema_version":"1.0"},"canonical_sha256":"089095f5681899e8bc3b0dd325e17e0e6814c17b1a066dc277e03e7fe3199da9","source":{"kind":"arxiv","id":"1904.00553","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.00553","created_at":"2026-05-17T23:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.00553v1","created_at":"2026-05-17T23:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00553","created_at":"2026-05-17T23:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"BCIJL5LIDCM6","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BCIJL5LIDCM6RPB3","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BCIJL5LI","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BCIJL5LIDCM6RPB3BXJSLYL6BZ","target":"record","payload":{"canonical_record":{"source":{"id":"1904.00553","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2019-04-01T04:12:57Z","cross_cats_sorted":[],"title_canon_sha256":"4cb2745dee86636ec0f8041c322f7290cfbac5f67d4f8b39dbd0374ff2a3a028","abstract_canon_sha256":"cddc50640033ee649db0f828bd2681ac1004ae5172313395acace060caafd15d"},"schema_version":"1.0"},"canonical_sha256":"089095f5681899e8bc3b0dd325e17e0e6814c17b1a066dc277e03e7fe3199da9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:48.107433Z","signature_b64":"8a9qoivxK9fW3h78dVCg9q3pS60mj7n2qzFP9cdJElPTv4x1/5hpUyivaa5mkpZtgxU3YHL2JCqd3nzSGtT0AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"089095f5681899e8bc3b0dd325e17e0e6814c17b1a066dc277e03e7fe3199da9","last_reissued_at":"2026-05-17T23:49:48.106696Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:48.106696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.00553","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1m5r11L8dF+vZ5/cUIy1yZGnZ4VGjYQJ4WSjjk1/zjiT1Z77+CuPH1Pj9EnHuszIlrdZg9tXe/ig7k9Ubw0yDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T09:34:47.370530Z"},"content_sha256":"12202c108924c775d7bc946e429dedebc33c4753566a2b3e3db76880f3aab6d6","schema_version":"1.0","event_id":"sha256:12202c108924c775d7bc946e429dedebc33c4753566a2b3e3db76880f3aab6d6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BCIJL5LIDCM6RPB3BXJSLYL6BZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Layered Image Compression using Scalable Auto-encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Chuanmin Jia, Siwei Ma, Wen Gao, Yao Wang, Zhaoyi Liu","submitted_at":"2019-04-01T04:12:57Z","abstract_excerpt":"This paper presents a novel convolutional neural network (CNN) based image compression framework via scalable auto-encoder (SAE). Specifically, our SAE based deep image codec consists of hierarchical coding layers, each of which is an end-to-end optimized auto-encoder. The coarse image content and texture are encoded through the first (base) layer while the consecutive (enhance) layers iteratively code the pixel-level reconstruction errors between the original and former reconstructed images. The proposed SAE structure alleviates the need to train multiple models for different bit-rate points "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00553","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2vYuoH3W1S2mq4yyXDv23e0Fq3kZeIr0aG9qtZ3l6yvj8M/KZUaA0MyRAxgyBN5LE3AlJI/F+oe94P7sOJY0CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T09:34:47.370880Z"},"content_sha256":"5b162d4f2c55c084b61dd804bbc68924cf672e4d0da1927cd31b4d1e9a94eb79","schema_version":"1.0","event_id":"sha256:5b162d4f2c55c084b61dd804bbc68924cf672e4d0da1927cd31b4d1e9a94eb79"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BCIJL5LIDCM6RPB3BXJSLYL6BZ/bundle.json","state_url":"https://pith.science/pith/BCIJL5LIDCM6RPB3BXJSLYL6BZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BCIJL5LIDCM6RPB3BXJSLYL6BZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-26T09:34:47Z","links":{"resolver":"https://pith.science/pith/BCIJL5LIDCM6RPB3BXJSLYL6BZ","bundle":"https://pith.science/pith/BCIJL5LIDCM6RPB3BXJSLYL6BZ/bundle.json","state":"https://pith.science/pith/BCIJL5LIDCM6RPB3BXJSLYL6BZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BCIJL5LIDCM6RPB3BXJSLYL6BZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BCIJL5LIDCM6RPB3BXJSLYL6BZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"cddc50640033ee649db0f828bd2681ac1004ae5172313395acace060caafd15d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2019-04-01T04:12:57Z","title_canon_sha256":"4cb2745dee86636ec0f8041c322f7290cfbac5f67d4f8b39dbd0374ff2a3a028"},"schema_version":"1.0","source":{"id":"1904.00553","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.00553","created_at":"2026-05-17T23:49:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.00553v1","created_at":"2026-05-17T23:49:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00553","created_at":"2026-05-17T23:49:48Z"},{"alias_kind":"pith_short_12","alias_value":"BCIJL5LIDCM6","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BCIJL5LIDCM6RPB3","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BCIJL5LI","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:5b162d4f2c55c084b61dd804bbc68924cf672e4d0da1927cd31b4d1e9a94eb79","target":"graph","created_at":"2026-05-17T23:49:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"This paper presents a novel convolutional neural network (CNN) based image compression framework via scalable auto-encoder (SAE). Specifically, our SAE based deep image codec consists of hierarchical coding layers, each of which is an end-to-end optimized auto-encoder. The coarse image content and texture are encoded through the first (base) layer while the consecutive (enhance) layers iteratively code the pixel-level reconstruction errors between the original and former reconstructed images. The proposed SAE structure alleviates the need to train multiple models for different bit-rate points ","authors_text":"Chuanmin Jia, Siwei Ma, Wen Gao, Yao Wang, Zhaoyi Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2019-04-01T04:12:57Z","title":"Layered Image Compression using Scalable Auto-encoder"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00553","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:12202c108924c775d7bc946e429dedebc33c4753566a2b3e3db76880f3aab6d6","target":"record","created_at":"2026-05-17T23:49:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"cddc50640033ee649db0f828bd2681ac1004ae5172313395acace060caafd15d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2019-04-01T04:12:57Z","title_canon_sha256":"4cb2745dee86636ec0f8041c322f7290cfbac5f67d4f8b39dbd0374ff2a3a028"},"schema_version":"1.0","source":{"id":"1904.00553","kind":"arxiv","version":1}},"canonical_sha256":"089095f5681899e8bc3b0dd325e17e0e6814c17b1a066dc277e03e7fe3199da9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"089095f5681899e8bc3b0dd325e17e0e6814c17b1a066dc277e03e7fe3199da9","first_computed_at":"2026-05-17T23:49:48.106696Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:48.106696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"8a9qoivxK9fW3h78dVCg9q3pS60mj7n2qzFP9cdJElPTv4x1/5hpUyivaa5mkpZtgxU3YHL2JCqd3nzSGtT0AA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:48.107433Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.00553","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:12202c108924c775d7bc946e429dedebc33c4753566a2b3e3db76880f3aab6d6","sha256:5b162d4f2c55c084b61dd804bbc68924cf672e4d0da1927cd31b4d1e9a94eb79"],"state_sha256":"5eba568930f9611b8d580b06d25306d1295d5732cae9c89cb1860f9ed2b01b36"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yMZ7WeXGZoP820dc1kjirppUIgQvUQA9K+n24udbEqiA679mjd8f1GLK8/h+eRz2sUYsIQ23Ez5bOnJpkkCpAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T09:34:47.372933Z","bundle_sha256":"e6ac870d8d47aaa320ec8c636e462c8c6969b8a1f1423cb1fc88dab276ed6368"}}