Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T05:35:01.787731Z
Paper Citation Record · LEDGER
As of 10 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 0 inbound Pith citation observations for arXiv:2607.22783.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T05:35:01.787731Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
79 of 79 outbound references displayed
External citation measurements
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Observation dfa42b2f-f305-4a40-b7ec-e1ebf7697e89 · outbound
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding The JPEG still picture compression standard,
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Observation 9a77b5c1-6aa9-407a-9491-d4216a826b56 · outbound
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Observation 7902a1e5-55dc-4b56-b677-dd0643dfaa1a · outbound
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding ColorVideoVDP: A visual difference predictor for image, video and display distortions,
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JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Subjective image quality assessment with boosted triplet comparisons,
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JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding On the mDCT-PSNR image quality index,
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JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Information technology — JPEG AIC Assessment of image coding — Part 3: Subjective quality assessment of high-fidelity images,
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Observation 6c7e635a-32c4-4d4e-86a5-2ab709b60bce · outbound
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Final call for proposals on objective image quality assessment (aic-4),
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Observation 7daa55b1-5b06-4d3a-aa1b-1c575624a7fb · outbound
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Statistical study on perceived JPEG image quality via MCL-JCI dataset construction and analysis,
Reference 31
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Observation 24a08c81-2237-4fea-8bde-7c6c4b3ac1ed · outbound
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding JND-Pano: Database for just noticeable difference of JPEG compressed panoramic images,
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Observation 0f352241-0b1e-46d4-86a6-389d958f81be · outbound
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Observation 4d1a3c30-f8b3-4929-b11e-033482cd9351 · outbound
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding JPEG AIC-3 dataset: towards defining the high quality to nearly visually lossless quality range,
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JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Crowdsourced estimation of collective just noticeable difference for compressed video with the flicker test and QUEST+,
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Observation 28b349a0-cff9-4c9e-b41e-d583d4c62054 · outbound
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JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding A new standard method of subjective assessment of barely visible image artifacts and a new public database,
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JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding DINOv2: learning robust visual features without supervision,
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