{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:GNBRSCARZW6QAM3MBDWDAP4UPF","short_pith_number":"pith:GNBRSCAR","canonical_record":{"source":{"id":"1612.08712","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2016-12-27T19:21:18Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5e957cce4626d0d12c86a06e08380e5327872a96ecad1aefc224550d0dfb9dd1","abstract_canon_sha256":"c608936c85ea901fd7607798a9963d628c3b042c15035a139b4cc8c9a31bb3be"},"schema_version":"1.0"},"canonical_sha256":"3343190811cdbd00336c08ec303f9479545a0d22708b9e31a186fe36aaa1976c","source":{"kind":"arxiv","id":"1612.08712","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.08712","created_at":"2026-05-18T00:47:42Z"},{"alias_kind":"arxiv_version","alias_value":"1612.08712v2","created_at":"2026-05-18T00:47:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.08712","created_at":"2026-05-18T00:47:42Z"},{"alias_kind":"pith_short_12","alias_value":"GNBRSCARZW6Q","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"GNBRSCARZW6QAM3M","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"GNBRSCAR","created_at":"2026-05-18T12:30:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:GNBRSCARZW6QAM3MBDWDAP4UPF","target":"record","payload":{"canonical_record":{"source":{"id":"1612.08712","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2016-12-27T19:21:18Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5e957cce4626d0d12c86a06e08380e5327872a96ecad1aefc224550d0dfb9dd1","abstract_canon_sha256":"c608936c85ea901fd7607798a9963d628c3b042c15035a139b4cc8c9a31bb3be"},"schema_version":"1.0"},"canonical_sha256":"3343190811cdbd00336c08ec303f9479545a0d22708b9e31a186fe36aaa1976c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:47:42.211643Z","signature_b64":"bCzRRW/tyiT3S7rAfPKErRI7ZBBRKzIl6UCnw11YbJ3MczWD2E2xXVXRy8qVX+BymV7Z3VY2ua9vEC/cJdWmBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3343190811cdbd00336c08ec303f9479545a0d22708b9e31a186fe36aaa1976c","last_reissued_at":"2026-05-18T00:47:42.210907Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:47:42.210907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.08712","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:47:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cUoQbSTVIm79xsDc0AvwMwchsFRGuV5YWk4g3XxwYQSGlTcXuSnkPJQJ6KbklINON90uf7WJTi88/YYlQwfcCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:17:59.929886Z"},"content_sha256":"09304578d46a2afc1d21d85ad52d97f3d6b9637833cf30f6d19b46979d007346","schema_version":"1.0","event_id":"sha256:09304578d46a2afc1d21d85ad52d97f3d6b9637833cf30f6d19b46979d007346"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:GNBRSCARZW6QAM3MBDWDAP4UPF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semantic Perceptual Image Compression using Deep Convolution Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.MM","authors_text":"Aaditya Prakash, Antonella DiLillo, James Storer, Nick Moran, Solomon Garber","submitted_at":"2016-12-27T19:21:18Z","abstract_excerpt":"It has long been considered a significant problem to improve the visual quality of lossy image and video compression. Recent advances in computing power together with the availability of large training data sets has increased interest in the application of deep learning cnns to address image recognition and image processing tasks. Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression. A modest increase in complexity is incorporated to the encoder which allows a standard, off-the-shelf jpeg decoder to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.08712","kind":"arxiv","version":2},"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":"2026-05-18T00:47:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mhafxkDh1VOMOrmaQBV0zqJ5acwQWOvhTSBRCyHmrXAfDjMOQ6V7pXQPq6FS4tzMPZMxHLxzxbIV4FJm0rKXBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:17:59.930608Z"},"content_sha256":"e42f2aa4f62e6b5fecb04e545db9fc366c49e50a6dee75ed0fec34658506f9fa","schema_version":"1.0","event_id":"sha256:e42f2aa4f62e6b5fecb04e545db9fc366c49e50a6dee75ed0fec34658506f9fa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GNBRSCARZW6QAM3MBDWDAP4UPF/bundle.json","state_url":"https://pith.science/pith/GNBRSCARZW6QAM3MBDWDAP4UPF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GNBRSCARZW6QAM3MBDWDAP4UPF/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":"2026-05-25T15:17:59Z","links":{"resolver":"https://pith.science/pith/GNBRSCARZW6QAM3MBDWDAP4UPF","bundle":"https://pith.science/pith/GNBRSCARZW6QAM3MBDWDAP4UPF/bundle.json","state":"https://pith.science/pith/GNBRSCARZW6QAM3MBDWDAP4UPF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GNBRSCARZW6QAM3MBDWDAP4UPF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:GNBRSCARZW6QAM3MBDWDAP4UPF","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":"c608936c85ea901fd7607798a9963d628c3b042c15035a139b4cc8c9a31bb3be","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2016-12-27T19:21:18Z","title_canon_sha256":"5e957cce4626d0d12c86a06e08380e5327872a96ecad1aefc224550d0dfb9dd1"},"schema_version":"1.0","source":{"id":"1612.08712","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.08712","created_at":"2026-05-18T00:47:42Z"},{"alias_kind":"arxiv_version","alias_value":"1612.08712v2","created_at":"2026-05-18T00:47:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.08712","created_at":"2026-05-18T00:47:42Z"},{"alias_kind":"pith_short_12","alias_value":"GNBRSCARZW6Q","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_16","alias_value":"GNBRSCARZW6QAM3M","created_at":"2026-05-18T12:30:19Z"},{"alias_kind":"pith_short_8","alias_value":"GNBRSCAR","created_at":"2026-05-18T12:30:19Z"}],"graph_snapshots":[{"event_id":"sha256:e42f2aa4f62e6b5fecb04e545db9fc366c49e50a6dee75ed0fec34658506f9fa","target":"graph","created_at":"2026-05-18T00:47:42Z","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":"It has long been considered a significant problem to improve the visual quality of lossy image and video compression. Recent advances in computing power together with the availability of large training data sets has increased interest in the application of deep learning cnns to address image recognition and image processing tasks. Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression. A modest increase in complexity is incorporated to the encoder which allows a standard, off-the-shelf jpeg decoder to ","authors_text":"Aaditya Prakash, Antonella DiLillo, James Storer, Nick Moran, Solomon Garber","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2016-12-27T19:21:18Z","title":"Semantic Perceptual Image Compression using Deep Convolution Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.08712","kind":"arxiv","version":2},"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":"sha256:09304578d46a2afc1d21d85ad52d97f3d6b9637833cf30f6d19b46979d007346","target":"record","created_at":"2026-05-18T00:47:42Z","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":"c608936c85ea901fd7607798a9963d628c3b042c15035a139b4cc8c9a31bb3be","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.MM","submitted_at":"2016-12-27T19:21:18Z","title_canon_sha256":"5e957cce4626d0d12c86a06e08380e5327872a96ecad1aefc224550d0dfb9dd1"},"schema_version":"1.0","source":{"id":"1612.08712","kind":"arxiv","version":2}},"canonical_sha256":"3343190811cdbd00336c08ec303f9479545a0d22708b9e31a186fe36aaa1976c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3343190811cdbd00336c08ec303f9479545a0d22708b9e31a186fe36aaa1976c","first_computed_at":"2026-05-18T00:47:42.210907Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:47:42.210907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bCzRRW/tyiT3S7rAfPKErRI7ZBBRKzIl6UCnw11YbJ3MczWD2E2xXVXRy8qVX+BymV7Z3VY2ua9vEC/cJdWmBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:47:42.211643Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.08712","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:09304578d46a2afc1d21d85ad52d97f3d6b9637833cf30f6d19b46979d007346","sha256:e42f2aa4f62e6b5fecb04e545db9fc366c49e50a6dee75ed0fec34658506f9fa"],"state_sha256":"c95e26bbdbd447aeb9c050ec0f4f2147fe826dab6cf1e2693c53b5d3dd6d21a6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WjxzH/u17qNga2WrEl6FYupUo93kK4hgTIl1w5FmoM7q5NSD5GGFcJPHruPoo3zJN3v9Eg4WJy2gEclGn527BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T15:17:59.934285Z","bundle_sha256":"5f0fd4b8876fb8210e74b39a926368a4831fa01309e3af09c3b7280100ae9363"}}