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Paper Citation Record · LEDGER

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding

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.

pith.paper-citation-record.v1
2607.22783 v1

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

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measured 79 of 79 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

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Reference resolution

79 of 79 outbound references displayed

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Outbound references

Observation dfa42b2f-f305-4a40-b7ec-e1ebf7697e89 · outbound

This paper cites The JPEG still picture compression standard,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding The JPEG still picture compression standard,

Reference 1

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Observation 0aa46f29-9117-4bcd-88da-9c4f1c438a47 · outbound

This paper cites An overview of the JPEG 2000 still image compression standard,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding An overview of the JPEG 2000 still image compression standard,

Reference 2

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Observation dd780237-d247-4ef0-abb1-b4ea833011fc · outbound

This paper cites Overview of the high efficiency video coding (HEVC) standard,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Overview of the high efficiency video coding (HEVC) standard,

Reference 3

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Observation 7db03f65-70e2-4f42-b0c2-5312c0706909 · outbound

This paper cites An evaluation of the next-generation image coding standard A VIF,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding An evaluation of the next-generation image coding standard A VIF,

Reference 4

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Observation 24e30797-1b91-4d18-b13f-71ddac77d370 · outbound

This paper cites Versatile video coding standard: A review from coding tools to consumers deployment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Versatile video coding standard: A review from coding tools to consumers deployment,

Reference 5

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Observation 955cf214-6caa-4c34-89c4-dc08a04f275c · outbound

This paper cites The JPEG XL Image Coding System: History, Features, Coding Tools, Design Rationale, and Future.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding The JPEG XL Image Coding System: History, Features, Coding Tools, Design Rationale, and Future

Reference 6

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Observation bcc1d9d3-f547-4061-bf25-82ba27dc8d8e · outbound

This paper cites Overview of variable rate coding in JPEG AI,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Overview of variable rate coding in JPEG AI,

Reference 7

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Observation 94eae8e5-08d5-4354-924b-338e6a01a5bb · outbound

This paper cites An overview of the JPEG AI learning-based image coding standard,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding An overview of the JPEG AI learning-based image coding standard,

Reference 8

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Observation d618774b-9fb5-47ea-bf1a-d66b59e4e96b · outbound

This paper cites Frequency-aware transformer for learned image compression,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Frequency-aware transformer for learned image compression,

Reference 9

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Observation f902c1b6-801e-4cdc-93fc-d270196fa70b · outbound

This paper cites Cool-chic: Coordinate-based low complexity hierarchical image codec,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Cool-chic: Coordinate-based low complexity hierarchical image codec,

Reference 10

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Observation 31b1fecf-884a-4a20-9897-6c73f0242e5d · outbound

This paper cites Cool-chic 5.0: Faster Encoding and Inter-Feature Entropy Modeling for Overfitted Image Compression.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Cool-chic 5.0: Faster Encoding and Inter-Feature Entropy Modeling for Overfitted Image Compression

Reference 11

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Observation 102338cf-7679-40dc-98e9-5f3371922e7b · outbound

This paper cites Good, cheap, and fast: Overfitted image compression with Wasserstein distortion,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Good, cheap, and fast: Overfitted image compression with Wasserstein distortion,

Reference 12

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Observation 8c3a61a5-6bce-4c72-a458-cb4f7477536b · outbound

This paper cites Generative adversarial networks for extreme learned image compres- sion,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Generative adversarial networks for extreme learned image compres- sion,

Reference 13

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Observation c6040c41-bbcc-458a-9e13-92e68d2752d9 · outbound

This paper cites Subjective visual quality assessment for high-fidelity learning-based image compression,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Subjective visual quality assessment for high-fidelity learning-based image compression,

Reference 14

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Observation 0d01e2ee-88bd-4d19-944d-d5a600c22d4f · outbound

This paper cites Image and video compression with neural networks: A review,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Image and video compression with neural networks: A review,

Reference 15

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Observation 2add4fbf-10ad-429f-a866-d6fa6320ef76 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Image quality assessment: from error visibility to structural similarity,

Reference 16

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Observation 9a77b5c1-6aa9-407a-9491-d4216a826b56 · outbound

This paper cites Toward a practical perceptual video quality metric,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Toward a practical perceptual video quality metric,

Reference 17

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Observation a404e29b-af7e-497a-bae8-7cc8fca99358 · outbound

This paper cites HDR- VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding HDR- VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions,

Reference 18

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Observation 7902a1e5-55dc-4b56-b677-dd0643dfaa1a · outbound

This paper cites ColorVideoVDP: A visual difference predictor for image, video and display distortions,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding ColorVideoVDP: A visual difference predictor for image, video and display distortions,

Reference 19

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Observation ad52f19b-f36f-445b-a8be-629677426058 · outbound

This paper cites A statistical evaluation of recent full reference image quality assessment algorithms,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding A statistical evaluation of recent full reference image quality assessment algorithms,

Reference 20

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Observation fa651a2a-1bf3-4fcb-8cc9-7769cf10675f · outbound

This paper cites Image database TID2013: Peculiarities, results and perspectives,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Image database TID2013: Peculiarities, results and perspectives,

Reference 21

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Observation 2c7c18e2-94ad-486c-a415-0122b558311a · outbound

This paper cites Fine-grained image quality assess- ment: A revisit and further thinking,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Fine-grained image quality assess- ment: A revisit and further thinking,

Reference 22

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Observation a98739d3-8524-44d9-9517-88a56acfc972 · outbound

This paper cites Fine-grained quality assessment for compressed images,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Fine-grained quality assessment for compressed images,

Reference 23

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Observation e9edbc16-6a23-411b-9e6b-c2707ebbda63 · outbound

This paper cites Percep- tual quality assessment for fine-grained compressed images,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Percep- tual quality assessment for fine-grained compressed images,

Reference 24

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Observation 8fcf1c93-d689-4d9d-a0f6-3abaf051e1ec · outbound

This paper cites 2AFC prompting of large multimodal models for image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding 2AFC prompting of large multimodal models for image quality assessment,

Reference 25

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Observation 07de96e7-ecc9-4c7a-aa82-b4b7de72913c · outbound

This paper cites Fine-grained HDR image quality assessment from noticeably distorted to very high fidelity,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Fine-grained HDR image quality assessment from noticeably distorted to very high fidelity,

Reference 26

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Observation 94de8402-e880-4155-9f51-9a6a83939d78 · outbound

This paper cites Subjective image quality assessment with boosted triplet comparisons,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Subjective image quality assessment with boosted triplet comparisons,

Reference 27

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Observation f7e0ba0f-1aeb-46f7-9f9b-f9d7135d3189 · outbound

This paper cites On the mDCT-PSNR image quality index,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding On the mDCT-PSNR image quality index,

Reference 28

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Observation 8fb5e527-4cf3-4e4e-b043-990993017487 · outbound

This paper cites Information technology — JPEG AIC Assessment of image coding — Part 3: Subjective quality assessment of high-fidelity images,.

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,

Reference 29

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Observation 6c7e635a-32c4-4d4e-86a5-2ab709b60bce · outbound

This paper cites Final call for proposals on objective image quality assessment (aic-4),.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Final call for proposals on objective image quality assessment (aic-4),

Reference 30

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Observation 7daa55b1-5b06-4d3a-aa1b-1c575624a7fb · outbound

This paper cites Statistical study on perceived JPEG image quality via MCL-JCI dataset construction and analysis,.

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

This paper cites JND-Pano: Database for just noticeable difference of JPEG compressed panoramic images,.

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,

Reference 32

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Observation 0f352241-0b1e-46d4-86a6-389d958f81be · outbound

This paper cites Picture-level just noticeable difference for symmetrically and asymmetrically compressed stereo- scopic images: Subjective quality assessment study and datasets,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Picture-level just noticeable difference for symmetrically and asymmetrically compressed stereo- scopic images: Subjective quality assessment study and datasets,

Reference 33

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Observation c20314c7-f7c7-44db-b924-278ce2cffaf3 · outbound

This paper cites A JND dataset based on VVC com- pressed images,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding A JND dataset based on VVC com- pressed images,

Reference 34

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Observation 5aac80e0-e7b6-4c88-a24a-1ae60878f597 · outbound

This paper cites Large-scale crowdsourced subjective assessment of picturewise just noticeable difference,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Large-scale crowdsourced subjective assessment of picturewise just noticeable difference,

Reference 35

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Observation 4d1a3c30-f8b3-4929-b11e-033482cd9351 · outbound

This paper cites JPEG AIC-3 dataset: towards defining the high quality to nearly visually lossless quality range,.

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,

Reference 36

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Observation 4b566080-9f28-4ea7-a30a-fc5e7fc63376 · outbound

This paper cites Crowdsourced estimation of collective just noticeable difference for compressed video with the flicker test and QUEST+,.

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+,

Reference 37

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Observation 06d12c8e-e2f9-4c72-bdc9-cd094c23682b · outbound

This paper cites Evaluation of objec- tive image quality metrics for high-fidelity image compression,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Evaluation of objec- tive image quality metrics for high-fidelity image compression,

Reference 38

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Observation 28b349a0-cff9-4c9e-b41e-d583d4c62054 · outbound

This paper cites Information technology — Advanced image coding and evaluation — Part 2: Evaluation procedure for nearly lossless coding,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Information technology — Advanced image coding and evaluation — Part 2: Evaluation procedure for nearly lossless coding,

Reference 39

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Observation 8bc82517-2d23-4a99-ae67-98e90992faf4 · outbound

This paper cites A new standard method of subjective assessment of barely visible image artifacts and a new public database,.

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,

Reference 40

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source=pdf_text observed=2026-08-01T05:34:57.785671Z digest=sha256:9b53354c4153da4003d834a5b25a3e05e683fa6a5073aaeebc8377837ce968dd

Observation 0b699c8b-4951-4cc3-bf55-f72134ccafd2 · outbound

This paper cites DINOv2: learning robust visual features without supervision,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding DINOv2: learning robust visual features without supervision,

Reference 41

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source=pdf_text observed=2026-08-01T05:34:57.984701Z digest=sha256:33731a2fcd37db339fd1863474e479ab9ae558cf81643942d92fab037d8f2d1e

Observation 8c34fe6b-e9b3-45d7-b421-48597842867b · outbound

This paper cites Toward a better quality metric for the video community,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Toward a better quality metric for the video community,

Reference 42

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Observation 19ed2090-8939-4411-a6db-62fae9ad81ae · outbound

This paper cites Butteraugli, a tool for measuring perceived differences be- tween images,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Butteraugli, a tool for measuring perceived differences be- tween images,

Reference 43

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source=pdf_text observed=2026-08-01T05:34:58.382009Z digest=sha256:0d1c35f61e5e1753ad22694cce86f18f5186b15b5ec6c8695723e0ec4acfdf20

Observation 5c0ad5d1-2872-480c-abc6-b03f2cfbf311 · outbound

This paper cites SSIMULACRA 2.1: Structural similarity unveiling lo- cal and compression related artifacts,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding SSIMULACRA 2.1: Structural similarity unveiling lo- cal and compression related artifacts,

Reference 44

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source=pdf_text observed=2026-08-01T05:34:58.508424Z digest=sha256:66ad9372d3dc84305312853f27fa3162a0be3fbaaa3507154071bec128ff743a

Observation 599120bd-2bb1-4620-9d20-e49e6e36b422 · outbound

This paper cites Common Test Conditions on Objective Image Quality Assessment v2.0,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Common Test Conditions on Objective Image Quality Assessment v2.0,

Reference 45

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source=pdf_text observed=2026-08-01T05:34:58.588517Z digest=sha256:4d5a2c196d79d3efe6547bcd08ce0329aef12f25c47d641b49776b0839fa10f1

Observation 6e0fb962-9ba7-4f46-9924-df12c501b6cb · outbound

This paper cites Evaluating quality metrics through the lenses of psychophysical measurements of low-level vision,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Evaluating quality metrics through the lenses of psychophysical measurements of low-level vision,

Reference 46

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source=pdf_text observed=2026-08-01T05:34:58.704690Z digest=sha256:9702061927670a7a36f1b57766d3d1c335bc79c229af1acd45c61b921bfe9e3b

Observation bffe06c3-bb14-4410-8263-2a888d024940 · outbound

This paper cites JPEG on STEROIDS: Common optimization techniques for JPEG image compression,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding JPEG on STEROIDS: Common optimization techniques for JPEG image compression,

Reference 47

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source=pdf_text observed=2026-08-01T05:34:58.794091Z digest=sha256:7761c5e4e4a13c9a19ec1f02d209c181b38846abc124d25bdcf1b04e51933db6

Observation fe7ffa9b-d52d-41da-ba4c-acd220380a27 · outbound

This paper cites JPEG AI Common Training and Test Conditions,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding JPEG AI Common Training and Test Conditions,

Reference 48

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source=pdf_text observed=2026-08-01T05:34:58.861833Z digest=sha256:0c5087a47e284b08719c8194d21f4754a710da50a0112921c39ed6757cf3c41f

Observation 3e713abf-2d47-46b1-a752-396e5783e308 · outbound

This paper cites an unresolved cited work.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Unresolved cited work

Reference 49

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source=pdf_text observed=2026-08-01T05:34:59.000318Z digest=sha256:a6cb4f1ff72fb85b2d25937f65f15fc358fcf05e7fc314defaad51dd48238a20

Observation 919d83de-b5f7-4959-8fbc-8d34f623b882 · outbound

This paper cites an unresolved cited work.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Unresolved cited work

Reference 50

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source=pdf_text observed=2026-08-01T05:34:59.066778Z digest=sha256:3852ce847ca5dfc42977732109f9c3811d1ae55db0c209b483cf72a15030a32b

Observation 5a183fca-53f4-4bff-a4be-f6aaefc3818e · outbound

This paper cites an unresolved cited work.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Unresolved cited work

Reference 51

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source=pdf_text observed=2026-08-01T05:34:59.144594Z digest=sha256:ab07a6499ea1312b11cc89b653675b0b3b9e3620b3d6e005cad3f039b18f09d3

Observation ef682ad9-75c8-4c9c-aa2a-b3548b000318 · outbound

This paper cites Users prefer Jpegli over same-sized libjpeg-turbo or MozJPEG.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Users prefer Jpegli over same-sized libjpeg-turbo or MozJPEG

Reference 52

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source=pdf_text observed=2026-08-01T05:34:59.273577Z digest=sha256:8c8eb5255089c349e5d9a827878b667c94cb9ff143e2da78eb508cbe8c63df9e

Observation cb2ae4bf-acbb-47f4-afd7-03e11764dd7a · outbound

This paper cites libwebp: WebP codec, version 1.2.4,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding libwebp: WebP codec, version 1.2.4,

Reference 53

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source=pdf_text observed=2026-08-01T05:34:59.353769Z digest=sha256:cc488d57b3e7e32e5479465df27f1f56393543e43f372bd0c44ada200ee2713f

Observation 0933ce9d-a8c5-4a02-a3be-ebb22e15668e · outbound

This paper cites pngquant, version 2.14.1,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding pngquant, version 2.14.1,

Reference 54

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source=pdf_text observed=2026-08-01T05:34:59.489898Z digest=sha256:2b7e7efbca4c6e807ba16330980d8d6ad896fd9cb0fa7f83d89961790b8a7661

Observation 920c27f3-adb9-41c4-b997-70a5a3adc256 · outbound

This paper cites New full-reference quality metrics based on HVS,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding New full-reference quality metrics based on HVS,

Reference 55

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source=pdf_text observed=2026-08-01T05:34:59.568448Z digest=sha256:53f32d83c8c9050c01e39626320ccf6706146f7f685cc5e6e4bc6e66176d0d4c

Observation 5fdff255-258c-4d0a-88c9-f9cd122f5cb8 · outbound

This paper cites RGBA structural similarity: DSSIM version 3.3.4,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding RGBA structural similarity: DSSIM version 3.3.4,

Reference 56

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source=pdf_text observed=2026-08-01T05:34:59.642293Z digest=sha256:5c9f2be1e8fea417c5b1ca7de8fd9d3eb184eb464befb54f34fa5355032c4f87

Observation 84e4071d-939b-47a4-97c0-e7bdb15848af · outbound

This paper cites Information content weighting for perceptual image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Information content weighting for perceptual image quality assessment,

Reference 57

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source=pdf_text observed=2026-08-01T05:34:59.710239Z digest=sha256:2b0e22b909871879b3f5802e80b9c2e5d93dfa01d1dde5437e393ba2664ae1d1

Observation 851a4725-b653-4f28-9491-7310bbffe067 · outbound

This paper cites FSIM: A feature similarity index for image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding FSIM: A feature similarity index for image quality assessment,

Reference 58

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source=pdf_text observed=2026-08-01T05:34:59.780726Z digest=sha256:c02a4bb167668e532ed074395ac0641792832ed8f846fb50c33760cd5473fde1

Observation b719ad30-851b-481b-be25-d3f2c04174db · outbound

This paper cites VMAF v1: Good is not good enough,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding VMAF v1: Good is not good enough,

Reference 59

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source=pdf_text observed=2026-08-01T05:34:59.852841Z digest=sha256:9420d872d0ca09bbc36fd72f85d66d58bf60d641a5581e1cd2d5105bf0a1c26e

Observation c8c7d07d-51f9-4aff-9625-74f2d3ea9499 · outbound

This paper cites Image information and visual quality,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Image information and visual quality,

Reference 60

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Observation 316b841f-d2fe-4a27-9287-7d2fb421d344 · outbound

This paper cites The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathemat- ical observations,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathemat- ical observations,

Reference 61

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source=pdf_text observed=2026-08-01T05:35:00.031873Z digest=sha256:85aa7386ace20f293338cfff1360c726b49b337f8b1fae6b822760281ccc98c7

Observation 1a21e68a-f349-45c7-ab7b-b47373b50092 · outbound

This paper cites VSI: A visual saliency-induced index for perceptual image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding VSI: A visual saliency-induced index for perceptual image quality assessment,

Reference 62

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source=pdf_text observed=2026-08-01T05:35:00.121666Z digest=sha256:c139f1257d2c55f00bfa8f5bdfb8476e2ec26d28b30b04d29862be1d442bf122

Observation 77d7b191-b98b-4395-ac65-97fa239c381f · outbound

This paper cites Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,

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source=pdf_text observed=2026-08-01T05:35:00.192865Z digest=sha256:d5ce65420c7e07a95b141105470fa10b82d3878e6c6d5d8d11d45015fce1bdf1

Observation 3a5cd7cb-ce85-41d8-bd85-3e76f9b1b9fe · outbound

This paper cites Perceptual image quality assessment using a normalized Laplacian pyramid,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Perceptual image quality assessment using a normalized Laplacian pyramid,

Reference 64

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source=pdf_text observed=2026-08-01T05:35:00.294134Z digest=sha256:bcd7528912163767e2a34bf4cef3114b5b8c8ce9df5ab245a8deed970c191d4e

Observation 6594aa1b-e50d-48c5-a107-2ed45730f389 · outbound

This paper cites A Haar wavelet-based perceptual similarity index for image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding A Haar wavelet-based perceptual similarity index for image quality assessment,

Reference 65

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source=pdf_text observed=2026-08-01T05:35:00.398117Z digest=sha256:41dacbd4b6b2f9b6ba7c6fb3277798d3f3d356534817a2d27d714bcb1d52f302

Observation c462a11e-bb37-456d-bfcd-d6ad2e1f8285 · outbound

This paper cites FLIP: A difference evaluator for alternating images.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding FLIP: A difference evaluator for alternating images

Reference 66

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source=pdf_text observed=2026-08-01T05:35:00.484661Z digest=sha256:8c931982dc4c500a027649ede0df9451eaf98e66c7d966603ba639735cfe4b46

Observation 26427a9d-b4b5-45ca-b4d2-eafce99961c8 · outbound

This paper cites HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding HDR-VDP-3: A multi-metric for predicting image differences, quality and contrast distortions in high dynamic range and regular content

Reference 67

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source=pdf_text observed=2026-08-01T05:35:00.617262Z digest=sha256:0cc96d97fdf19009912256156b8b2e725a0603f64367afec0773347ff97694d2

Observation f7eb3cf2-1020-4ef7-b382-9861ee088df2 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding The unreasonable effectiveness of deep features as a perceptual metric,

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source=pdf_text observed=2026-08-01T05:35:00.787833Z digest=sha256:58b9e16df2d539dae1c62b3fb402525eb2763cf87977772efbe6544649a25e16

Observation 76473f1f-f2f8-4e10-a994-cb3ed930aecd · outbound

This paper cites Shift-tolerant perceptual similarity metric,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Shift-tolerant perceptual similarity metric,

Reference 69

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source=pdf_text observed=2026-08-01T05:35:00.956138Z digest=sha256:99d90fda998db848ce8b9d08fd717e0427d65cbfea6ee8b05c5bdbd3c54b1fd5

Observation 3d3e14cb-e956-49f4-b81b-5d1d2df9c6c3 · outbound

This paper cites PieAPP: Perceptual image- error assessment through pairwise preference,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding PieAPP: Perceptual image- error assessment through pairwise preference,

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source=pdf_text observed=2026-08-01T05:35:01.074510Z digest=sha256:7a66ce709fe2bfc64dd53cd4f9ab144f94276459f2e6e8d12b5201c5a2bf7408

Observation c7f2c1e4-98e2-48f9-8924-e6a6915b96b0 · outbound

This paper cites Deep neural networks for no-reference and full-reference image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Deep neural networks for no-reference and full-reference image quality assessment,

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source=pdf_text observed=2026-08-01T05:35:01.219228Z digest=sha256:91c45c1eb8f7c080032b8e3a81854adbdd6bf54c39c1cf48e1a0c34627ae3f14

Observation 037e9833-17a1-49d1-9d55-c752a57577d2 · outbound

This paper cites Image quality assessment: Unifying structure and texture similarity,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Image quality assessment: Unifying structure and texture similarity,

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source=pdf_text observed=2026-08-01T05:35:01.310720Z digest=sha256:29d1075c14fabdaaf6d5b11dbe69c1c8f9d044ebc026465a48b672b273498a2d

Observation 5c599c92-6021-4f5b-a44f-7da797828861 · outbound

This paper cites Attentions help CNNs see better: Attention-based hybrid image quality assessment network,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Attentions help CNNs see better: Attention-based hybrid image quality assessment network,

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source=pdf_text observed=2026-08-01T05:35:01.375575Z digest=sha256:da7bc4238b4aa38b678041d34046c6455474b42e6514ac39823e0d4e366816ed

Observation ec40d8e5-3f3c-4898-8975-16ea84b4b870 · outbound

This paper cites DeepDC: Deep distance correlation as a perceptual image quality evaluator,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding DeepDC: Deep distance correlation as a perceptual image quality evaluator,

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This paper cites DreamSim: Learning new dimensions of human visual similarity using synthetic data,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding DreamSim: Learning new dimensions of human visual similarity using synthetic data,

Reference 75

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This paper cites TOPIQ: A top-down approach from semantics to distortions for image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding TOPIQ: A top-down approach from semantics to distortions for image quality assessment,

Reference 76

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Observation 2db276c8-b17f-4437-a656-fd1d46e28554 · outbound

This paper cites Wasserstein distortion: Unifying fidelity and realism,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Wasserstein distortion: Unifying fidelity and realism,

Reference 77

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Observation 90528407-8fea-4ddc-9c37-af5e91079e0e · outbound

This paper cites Toward generalized image quality assessment: Relaxing the perfect reference quality assumption,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Toward generalized image quality assessment: Relaxing the perfect reference quality assumption,

Reference 78

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This paper cites Debiased mapping for full-reference image quality assessment,.

JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding Debiased mapping for full-reference image quality assessment,

Reference 79

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