Paper Citation Record · LEDGER

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

As of 21 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:1907.02665.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1907.02665 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T02:23:43.589590Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:55:55.098456Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T10:56:01.116353Z

Reference resolution

45 of 45 outbound references displayed

verified exact4
verified fuzzy40
unresolved1
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7cb75228-a97b-4b45-9a29-8111f50efb72 · outbound

This paper cites an unresolved cited work.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Unresolved cited work

Reference 1

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Source-reported events for the cited work

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Observation 2a64d981-384c-4f73-880c-f69ffc9baea4 · outbound

This paper cites End-to-end Optimized Image Compression.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network End-to-end Optimized Image Compression

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-25T02:25:14.870222Z

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Observation f4d7c44e-95c3-4303-a88e-7b1b0649ec32 · outbound

This paper cites Quality-of-experience of adaptive video streaming: Exploring the space of adaptations.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Quality-of-experience of adaptive video streaming: Exploring the space of adaptations

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.044802Z

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Observation dcaf1f82-a80b-4d35-9d2c-8d3ec135c421 · outbound

This paper cites Wang and A.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Wang and A

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.041607Z

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Observation ef3f471d-ed2f-4e9c-be54-28b7511f8a99 · outbound

This paper cites Display device-adapted video quality-of-experience assessment.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Display device-adapted video quality-of-experience assessment

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.066035Z

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Observation 8e0b9c78-2bed-4e8b-90b1-f54c651b1324 · outbound

This paper cites Reduced-and no-reference image quality assessment: The natural scene statistic model approach.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Reduced-and no-reference image quality assessment: The natural scene statistic model approach

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.073565Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b5761248-85f2-4ea9-a5bb-d26660ec0b6a · outbound

This paper cites No-reference image quality assessment in the spatial domain.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network No-reference image quality assessment in the spatial domain

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.054908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 556f2d65-4ccb-4e2a-98c9-0f8dc88b1341 · outbound

This paper cites Unsupervised feature learning framework for no-reference image quality assessment.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Unsupervised feature learning framework for no-reference image quality assessment

Reference 8

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Observation ff0a4b41-3b49-4e95-ad78-d7e6bb1b2eb6 · outbound

This paper cites Convolutional neural networks for no-reference image quality assessment.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Convolutional neural networks for no-reference image quality assessment

Reference 9

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Observation d175e1fa-27da-425f-bc18-d9fd14febffb · outbound

This paper cites End-to- end blind image quality assessment using deep neural networks.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network End-to- end blind image quality assessment using deep neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.002076Z

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Observation 3af1dd91-04bd-4a2e-80ff-0f3da7492784 · outbound

This paper cites ImageNet: A large-scale hierarchical image database.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network ImageNet: A large-scale hierarchical image database

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.988012Z

Source-reported events for the cited work

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Observation 81f618c1-77f9-4396-b33a-04ad49f74454 · outbound

This paper cites Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.019101Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c45f3de1-e633-42ed-bef5-ed97e4bc2aca · outbound

This paper cites Massive online crowdsourced study of subjective and objective picture quality.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Massive online crowdsourced study of subjective and objective picture quality

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.012355Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9bc46064-767a-45d3-89f5-0c3c13ae6e49 · outbound

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

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network A statistical evaluation of recent full reference image quality assessment algorithms

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.948975Z

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Observation cca571cb-86eb-40d5-bdfc-158805d7a4a6 · outbound

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

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Image database TID2013: Peculiarities, results and perspectives

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.976047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 46dda949-3611-460d-81d6-09010ee0ac30 · outbound

This paper cites Fully deep blind image quality predictor.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Fully deep blind image quality predictor

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.051892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 71c60374-119f-423a-bf5c-02ac18da5486 · outbound

This paper cites Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.058469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d4daf396-5961-4ff8-b7d1-24201775a187 · outbound

This paper cites Waterloo Exploration Database: New challenges for image quality assessment models.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Waterloo Exploration Database: New challenges for image quality assessment models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.048576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e72aa1ef-9beb-4dce-9f7e-4661cfe4b3ad · outbound

This paper cites The Pascal Visual Object Classes (VOC) Challenge.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network The Pascal Visual Object Classes (VOC) Challenge

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.061598Z

Source-reported events for the cited work

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Observation 82d17954-ee64-43e4-bb5e-075b35c4c09f · outbound

This paper cites Perceptual quality prediction on authentically distorted images using a bag of features approach.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Perceptual quality prediction on authentically distorted images using a bag of features approach

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.069917Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 599ded03-e11f-440c-b37a-1d5b75ccc360 · outbound

This paper cites Very deep convolutional networks for large-scale image recognition.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Very deep convolutional networks for large-scale image recognition

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.026429Z

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Observation 2c1f05de-f180-43ae-a076-e24d171450e6 · outbound

This paper cites Bilinear CNN models for ﬁne-grained visual recognition.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Bilinear CNN models for ﬁne-grained visual recognition

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.015762Z

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Observation 41d2ede8-4b7d-458d-a44b-e19a1a755373 · outbound

This paper cites Group MAD competition − a new methodology to compare objective image quality models.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Group MAD competition − a new methodology to compare objective image quality models

Reference 23

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Observation 92fc3f07-6594-4e10-8a4f-3797a1bea601 · outbound

This paper cites Deep neural networks for no-reference and full-reference image quality as- sessment.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Deep neural networks for no-reference and full-reference image quality as- sessment

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.996049Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 57917aa4-8a91-4d76-bf8d-fd5e586631af · outbound

This paper cites dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.984698Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d439b571-6a47-4e5e-9cc6-1457f6c49885 · outbound

This paper cites Blind image quality assessment using semi-supervised rectiﬁer networks.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Blind image quality assessment using semi-supervised rectiﬁer networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.994652Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e4e9de7f-d179-4825-80f6-fd64ff1b9b7a · outbound

This paper cites On the Use of Deep Learning for Blind Image Quality Assessment.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network On the Use of Deep Learning for Blind Image Quality Assessment

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-25T02:25:14.863579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0d019846-f8da-41da-8ae0-3b65dd495b6e · outbound

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

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network FSIM: A feature similarity index for image quality assessment

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.009084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5f5c5a75-b417-4482-adaa-ca21554602c1 · outbound

This paper cites Perceptual quality assessment for multi-exposure image fusion.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Perceptual quality assessment for multi-exposure image fusion

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.029447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e99d6df7-574d-44be-9ab5-74c0de360c04 · outbound

This paper cites Separating style and content.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Separating style and content

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.952177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f9f0b4b9-fe36-4b73-9716-62248bc0d23c · outbound

This paper cites Two-stream convolutional networks for action recognition in videos.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Two-stream convolutional networks for action recognition in videos

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.959046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd2d50fd-29e1-4952-b4c1-b037487c19a2 · outbound

This paper cites Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-05-25T02:25:14.852001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d8a10ea1-5ef0-4c8f-b48a-656a601349e7 · outbound

This paper cites Deep residual learning for image recognition.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Deep residual learning for image recognition

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.965434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ee2b032-3132-4966-b370-d82906c9d298 · outbound

This paper cites A Riemannian framework for tensor computing.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network A Riemannian framework for tensor computing

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.955544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aef6fc79-2dc1-4197-803a-4dacb08c2c70 · outbound

This paper cites Most apparent distortion: Full- reference image quality assessment and the role of strategy.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Most apparent distortion: Full- reference image quality assessment and the role of strategy

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.962569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 43c3f57d-df51-49c5-8f75-6601967e57c1 · outbound

This paper cites Objective quality assessment of multiply distorted images.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Objective quality assessment of multiply distorted images

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.968639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4fd3808e-9d54-4740-998f-97546e854e8c · outbound

This paper cites Final report from the video quality experts group on the validation of objective models of video quality assessment.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Final report from the video quality experts group on the validation of objective models of video quality assessment

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:33.972605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5dea2a0c-6db0-459b-84a1-d311ff2501ab · outbound

This paper cites Delving deep into rectiﬁers: Surpassing human-level performance on ImageNet classiﬁcation.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Delving deep into rectiﬁers: Surpassing human-level performance on ImageNet classiﬁcation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.079890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f24dca21-ae92-49c4-878f-fda89c331cc4 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Adam: A Method for Stochastic Optimization

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-25T02:25:14.857614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 71acdf4f-d38a-4bfe-bbeb-979fd74e8d5f · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Batch normalization: Accelerating deep network training by reducing internal covariate shift

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.083034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5dd4b5f5-8110-476b-a5e7-05124eda272b · outbound

This paper cites MatConvNet: Convolutional neural networks for Matlab.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network MatConvNet: Convolutional neural networks for Matlab

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.076701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e8919f96-75f8-438a-aa01-821cacbbbaa0 · outbound

This paper cites Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.023052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 78c3197e-7fdd-4730-9d77-3a4c4fe28fe3 · outbound

This paper cites Blind image quality assessment based on high order statistics aggregation.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Blind image quality assessment based on high order statistics aggregation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.032671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 83487ee2-eaaa-4d51-9c00-b50c40fe6996 · outbound

This paper cites Maximum differentiation (MAD) competition: A methodology for comparing computational models of perceptual quantities.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Maximum differentiation (MAD) competition: A methodology for comparing computational models of perceptual quantities

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.035738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 811f1662-2fe3-4259-813a-6b9ce2d88846 · outbound

This paper cites Compact bilinear pool- ing.

Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network Compact bilinear pool- ing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T02:26:34.038545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation bf867e90-610d-4b37-a201-bab05c1e399d · inbound

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA cites this paper.

Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:56:01.185604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.