{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:N5YEO34UCQ2E7V2YBZD4T3QDWX","short_pith_number":"pith:N5YEO34U","schema_version":"1.0","canonical_sha256":"6f70476f9414344fd7580e47c9ee03b5f4f822f53803d13a97936cdc4abdb4c6","source":{"kind":"arxiv","id":"2206.06541","version":1},"attestation_state":"computed","paper":{"title":"Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.MM"],"primary_cat":"eess.IV","authors_text":"Anant Baijal, Cheul-hee Hahm, Ilhyun Cho, Jayoon Koo, Namuk Kim, Wook-Hyung Kim","submitted_at":"2022-06-14T01:24:25Z","abstract_excerpt":"Deep-learning based techniques have contributed to the remarkable progress in the field of automatic image quality assessment (IQA). Existing IQA methods are designed to measure the quality of an image in terms of Mean Opinion Score (MOS) at the image-level (i.e. the whole image) or at the patch-level (dividing the image into multiple units and measuring quality of each patch). Some applications may require assessing the quality at the pixel-level (i.e. MOS value for each pixel), however, this is not possible in case of existing techniques as the spatial information is lost owing to their netw"},"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":"2206.06541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-06-14T01:24:25Z","cross_cats_sorted":["cs.CV","cs.MM"],"title_canon_sha256":"831e378d6dd3d622ee26adca2c2328dcadeb27b7e40eaf98528b6aa72bc77676","abstract_canon_sha256":"238273ce07ae1b3a92ba0e80b875e686fd3e79478c1bf67b7ff866fa1ee64d28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:31:43.356576Z","signature_b64":"mMLiTmrAQVgjryRr49OLbDxrxN3CResEdSZX4tmF/m509zTx2wC7ZrYm1bnuWN6OTkOY6y9JU+RbLNdkrTD5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f70476f9414344fd7580e47c9ee03b5f4f822f53803d13a97936cdc4abdb4c6","last_reissued_at":"2026-07-05T04:31:43.356088Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:31:43.356088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.MM"],"primary_cat":"eess.IV","authors_text":"Anant Baijal, Cheul-hee Hahm, Ilhyun Cho, Jayoon Koo, Namuk Kim, Wook-Hyung Kim","submitted_at":"2022-06-14T01:24:25Z","abstract_excerpt":"Deep-learning based techniques have contributed to the remarkable progress in the field of automatic image quality assessment (IQA). Existing IQA methods are designed to measure the quality of an image in terms of Mean Opinion Score (MOS) at the image-level (i.e. the whole image) or at the patch-level (dividing the image into multiple units and measuring quality of each patch). Some applications may require assessing the quality at the pixel-level (i.e. MOS value for each pixel), however, this is not possible in case of existing techniques as the spatial information is lost owing to their netw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.06541","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/2206.06541/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":"2206.06541","created_at":"2026-07-05T04:31:43.356153+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.06541v1","created_at":"2026-07-05T04:31:43.356153+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.06541","created_at":"2026-07-05T04:31:43.356153+00:00"},{"alias_kind":"pith_short_12","alias_value":"N5YEO34UCQ2E","created_at":"2026-07-05T04:31:43.356153+00:00"},{"alias_kind":"pith_short_16","alias_value":"N5YEO34UCQ2E7V2Y","created_at":"2026-07-05T04:31:43.356153+00:00"},{"alias_kind":"pith_short_8","alias_value":"N5YEO34U","created_at":"2026-07-05T04:31:43.356153+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/N5YEO34UCQ2E7V2YBZD4T3QDWX","json":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX.json","graph_json":"https://pith.science/api/pith-number/N5YEO34UCQ2E7V2YBZD4T3QDWX/graph.json","events_json":"https://pith.science/api/pith-number/N5YEO34UCQ2E7V2YBZD4T3QDWX/events.json","paper":"https://pith.science/paper/N5YEO34U"},"agent_actions":{"view_html":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX","download_json":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX.json","view_paper":"https://pith.science/paper/N5YEO34U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.06541&json=true","fetch_graph":"https://pith.science/api/pith-number/N5YEO34UCQ2E7V2YBZD4T3QDWX/graph.json","fetch_events":"https://pith.science/api/pith-number/N5YEO34UCQ2E7V2YBZD4T3QDWX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX/action/storage_attestation","attest_author":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX/action/author_attestation","sign_citation":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX/action/citation_signature","submit_replication":"https://pith.science/pith/N5YEO34UCQ2E7V2YBZD4T3QDWX/action/replication_record"}},"created_at":"2026-07-05T04:31:43.356153+00:00","updated_at":"2026-07-05T04:31:43.356153+00:00"}