{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KT6VSG6WWQSC5H6I3KWZU5VTVN","short_pith_number":"pith:KT6VSG6W","schema_version":"1.0","canonical_sha256":"54fd591bd6b4242e9fc8daad9a76b3ab7dd67813ac33bc881297a66829766511","source":{"kind":"arxiv","id":"2410.11506","version":3},"attestation_state":"computed","paper":{"title":"Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongyu An, Li Zhang, Ruiqin Xiong, Shijie Zhao, Xinfeng Zhang","submitted_at":"2024-10-15T11:17:19Z","abstract_excerpt":"Omnidirectional videos (ODVs) provide an immersive visual experience by capturing the 360{\\deg} scene. With the rapid advancements in virtual/augmented reality, metaverse, and generative artificial intelligence, the demand for high-quality ODVs is surging. However, ODVs often suffer from low resolution due to their wide field of view and limitations in capturing devices and transmission bandwidth. Although video super-resolution (SR) is a capable video quality enhancement technique, the performance ceiling and practical generalization of existing methods are limited when applied to ODVs due to"},"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":"2410.11506","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-10-15T11:17:19Z","cross_cats_sorted":[],"title_canon_sha256":"1d4d16660f742c8f7c551be7302309d26b1da96441b34650f6b284a5b37000a6","abstract_canon_sha256":"98ebf1d296fda5779bbfb58bae73b627b7ed890e0d15bdf9c8e988a7b2554924"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:04.554736Z","signature_b64":"MlzhQz4mIum+xanXX4cr4Wq31bqlWED7WK+M53sipHAd1mFZ7sMMLiiDibF3Q48P1S5meZDH9ete9LCuJfomCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54fd591bd6b4242e9fc8daad9a76b3ab7dd67813ac33bc881297a66829766511","last_reissued_at":"2026-07-05T11:49:04.554280Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:04.554280Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongyu An, Li Zhang, Ruiqin Xiong, Shijie Zhao, Xinfeng Zhang","submitted_at":"2024-10-15T11:17:19Z","abstract_excerpt":"Omnidirectional videos (ODVs) provide an immersive visual experience by capturing the 360{\\deg} scene. With the rapid advancements in virtual/augmented reality, metaverse, and generative artificial intelligence, the demand for high-quality ODVs is surging. However, ODVs often suffer from low resolution due to their wide field of view and limitations in capturing devices and transmission bandwidth. Although video super-resolution (SR) is a capable video quality enhancement technique, the performance ceiling and practical generalization of existing methods are limited when applied to ODVs due to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11506","kind":"arxiv","version":3},"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/2410.11506/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":"2410.11506","created_at":"2026-07-05T11:49:04.554343+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.11506v3","created_at":"2026-07-05T11:49:04.554343+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11506","created_at":"2026-07-05T11:49:04.554343+00:00"},{"alias_kind":"pith_short_12","alias_value":"KT6VSG6WWQSC","created_at":"2026-07-05T11:49:04.554343+00:00"},{"alias_kind":"pith_short_16","alias_value":"KT6VSG6WWQSC5H6I","created_at":"2026-07-05T11:49:04.554343+00:00"},{"alias_kind":"pith_short_8","alias_value":"KT6VSG6W","created_at":"2026-07-05T11:49:04.554343+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06710","citing_title":"A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN","json":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN.json","graph_json":"https://pith.science/api/pith-number/KT6VSG6WWQSC5H6I3KWZU5VTVN/graph.json","events_json":"https://pith.science/api/pith-number/KT6VSG6WWQSC5H6I3KWZU5VTVN/events.json","paper":"https://pith.science/paper/KT6VSG6W"},"agent_actions":{"view_html":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN","download_json":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN.json","view_paper":"https://pith.science/paper/KT6VSG6W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.11506&json=true","fetch_graph":"https://pith.science/api/pith-number/KT6VSG6WWQSC5H6I3KWZU5VTVN/graph.json","fetch_events":"https://pith.science/api/pith-number/KT6VSG6WWQSC5H6I3KWZU5VTVN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN/action/storage_attestation","attest_author":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN/action/author_attestation","sign_citation":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN/action/citation_signature","submit_replication":"https://pith.science/pith/KT6VSG6WWQSC5H6I3KWZU5VTVN/action/replication_record"}},"created_at":"2026-07-05T11:49:04.554343+00:00","updated_at":"2026-07-05T11:49:04.554343+00:00"}