Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions | Paper Citation Record

Observation 98c9c436-761f-4d3c-88c8-8a6b50fc4329 · outbound

This paper cites 6-DoF image local- ization from massive geo-tagged reference images,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions 6-DoF image local- ization from massive geo-tagged reference images,

Reference 1

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Observation 4eacf7e0-fc4f-4cd5-83cf-bc69f45c8dc6 · outbound

This paper cites Reduced reference quality assess- ment of light field images,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Reduced reference quality assess- ment of light field images,

Reference 2

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Observation 59bd6969-1798-481d-b2b0-75fb420ead61 · outbound

This paper cites Immersive light field video with a layered mesh representation,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Immersive light field video with a layered mesh representation,

Reference 3

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This paper cites Apple invents a light field panorama camera system for idevices and hmd that will create immersive scenes with 6 degrees of freedom,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Apple invents a light field panorama camera system for idevices and hmd that will create immersive scenes with 6 degrees of freedom,

Reference 4

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Observation 0d6c4c42-961f-4da6-a268-91af9c9cea65 · outbound

This paper cites Eye-sensing light field display: Delivering 3D creators’ visions to customers the way they intended,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Eye-sensing light field display: Delivering 3D creators’ visions to customers the way they intended,

Reference 5

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Observation 868d6c29-ca34-466e-b018-cbc76c488a4a · outbound

This paper cites BELIF: Blind quality evaluator of light field image with tensor structure variation index,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions BELIF: Blind quality evaluator of light field image with tensor structure variation index,

Reference 6

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Observation 87f051f9-57f9-4a41-81ba-d26e6bb3377b · outbound

This paper cites No-reference light field image quality assessment based on spatial-angular measurement,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions No-reference light field image quality assessment based on spatial-angular measurement,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:03.035879Z

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Observation 13396eae-8ada-40c2-b83f-3a249a68557d · outbound

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

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Image quality assessment: from error visibility to structural similarity,

Reference 8

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Observation c34dbba0-948e-4567-a64c-616c3e269c72 · outbound

This paper cites Light field image processing: An overview,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Light field image processing: An overview,

Reference 9

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Observation aa5fe9f9-15ec-45b2-8031-d2a18bc42c70 · outbound

This paper cites Learning-based view synthesis for light field cameras,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Learning-based view synthesis for light field cameras,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.991314Z

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Observation c3374e0c-9d10-4313-b95b-5a06d042c439 · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 11

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Observation 33c47b40-0d21-481d-a305-0dbb1d382432 · outbound

This paper cites Principles of light field imaging: Briefly revisiting 25 years of research,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Principles of light field imaging: Briefly revisiting 25 years of research,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.966283Z

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Observation 04d99212-10c7-40e6-a5ac-a2efed5567ca · outbound

This paper cites Multiscale structural similarity for image quality assessment,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Multiscale structural similarity for image quality assessment,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.950758Z

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Observation f11dd862-edd3-45af-998d-624c79cd9a96 · outbound

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

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions FSIM: A feature similarity index for image quality assessment,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.936378Z

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Observation 320ef1fe-e770-4a5a-88e9-0e81dd16dbfe · outbound

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

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Information content weighting for perceptual image quality assessment,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.921063Z

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Observation f9494686-c9df-4ecb-b3a4-c05407c9339e · outbound

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

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions VSI: A visual saliency-induced index for perceptual image quality assessment,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.905960Z

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Observation e85f4073-a154-4d08-aa76-96b0042ea55e · outbound

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

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,

Reference 17

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Observation 67dd5e70-6bba-498e-b8b6-3b27f2486b76 · outbound

This paper cites Efficient no-reference quality as- sessment and classification model for contrast distorted images,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Efficient no-reference quality as- sessment and classification model for contrast distorted images,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.879939Z

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Observation e5c0ed58-d946-488d-b529-3db074e735bc · outbound

This paper cites Blind image quality esti- mation via distortion aggravation,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Blind image quality esti- mation via distortion aggravation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.859410Z

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Observation 0a22207d-4364-4313-b0bd-fcf946db4ab8 · outbound

This paper cites Modeling the screen content image quality via multiscale edge attention similarity,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Modeling the screen content image quality via multiscale edge attention similarity,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.841401Z

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Observation d5b6c1a4-3779-4c2f-9651-ac396f5729b7 · outbound

This paper cites Blind image quality assessment: From natural scene statistics to perceptual quality,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Blind image quality assessment: From natural scene statistics to perceptual quality,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.825501Z

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Observation 346d4049-8d05-4e7b-bb67-30eb0b622389 · outbound

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

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions No-reference image quality assessment in the spatial domain,

Reference 22

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Observation 143813f6-676b-421c-b1be-d0032df7eb55 · outbound

This paper cites A no-reference objective image sharpness metric based on the notion of just noticeable blur (jnb),.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions A no-reference objective image sharpness metric based on the notion of just noticeable blur (jnb),

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.796406Z

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Observation 1975ec96-ef86-4865-8889-3fdfdf7843a0 · outbound

This paper cites No-reference visually significant blocking artifact metric for natural scene images,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions No-reference visually significant blocking artifact metric for natural scene images,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.779176Z

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Observation 67bd64ef-546b-4f13-91ab-2d8db3dfe370 · outbound

This paper cites An improved perception-based no- reference objective image sharpness metric using iterative edge refine- ment,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions An improved perception-based no- reference objective image sharpness metric using iterative edge refine- ment,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.762778Z

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Observation 47f26311-214d-4c61-9db5-d97444cf8f90 · outbound

This paper cites Tensor oriented no-reference light field image quality assessment,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Tensor oriented no-reference light field image quality assessment,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.745671Z

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Observation 754dce19-0153-45eb-9e47-aa456cf7df2d · outbound

This paper cites Light field image quality assessment via the light field coherence,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Light field image quality assessment via the light field coherence,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.727576Z

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Observation f0656dda-dedb-439e-8c34-945d4bdd49d7 · outbound

This paper cites Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks

Reference 28

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Observation 324f7cb6-80cb-4056-8038-cd3732b2bd82 · outbound

This paper cites Resnet in Resnet: Generalizing Residual Architectures.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Resnet in Resnet: Generalizing Residual Architectures

Reference 29

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Observation b199afe2-8dff-4983-b1a4-05e163732afd · outbound

This paper cites An Overview of Multi-Task Learning in Deep Neural Networks.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions An Overview of Multi-Task Learning in Deep Neural Networks

Reference 30

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Observation 97f33853-6622-4135-8c87-529bd00e6041 · outbound

This paper cites Multi- task rank learning for image quality assessment,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Multi- task rank learning for image quality assessment,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.710254Z

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Observation 1381b536-453b-4232-9ad4-f43cdf6f93d5 · outbound

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

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions End-to- end blind image quality assessment using deep neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.695263Z

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Observation 4928fcc6-2641-46c9-8dfb-1f0599985e12 · outbound

This paper cites Naturalness-aware deep no-reference image quality assessment,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Naturalness-aware deep no-reference image quality assessment,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.678846Z

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Observation 1734539d-a843-4d32-bf52-8642ec8371c5 · outbound

This paper cites Personality-assisted multi- task learning for generic and personalized image aesthetics assessment,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Personality-assisted multi- task learning for generic and personalized image aesthetics assessment,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.661188Z

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Observation c1686390-8731-4de7-99da-91c09125842d · outbound

This paper cites Reinforcement Learning with Unsupervised Auxiliary Tasks.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Reinforcement Learning with Unsupervised Auxiliary Tasks

Reference 35

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Observation 08832d7b-af81-4b4c-8073-b8740e2f448d · outbound

This paper cites Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Multi-task learning using uncer- tainty to weigh losses for scene geometry and semantics,

Reference 36

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Observation 8150c3fb-0f1c-4f2a-aabe-75557e8ad4bf · outbound

This paper cites Perceptual evaluation of light field image,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Perceptual evaluation of light field image,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.633750Z

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Observation e9874af3-4cc8-4464-a083-1860e242280a · outbound

This paper cites Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.618938Z

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Observation 6eff3ae1-3074-4237-9bd1-f6294d75eabd · outbound

This paper cites Lightweight deep residual CNN for fault diagnosis of rotating machinery based on depthwise separable convolutions,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Lightweight deep residual CNN for fault diagnosis of rotating machinery based on depthwise separable convolutions,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.604098Z

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Observation 6f3b5f28-535c-413c-ad36-671b5ee6708f · outbound

This paper cites XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture Classification.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture Classification

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-11T19:16:02.366248Z

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Observation 5388eff6-0689-4b6f-b391-2c9eb53437a8 · outbound

This paper cites Improved image classification with 4D light-field and interleaved con- volutional neural network,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Improved image classification with 4D light-field and interleaved con- volutional neural network,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.588933Z

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Observation 0cb0a402-5ffe-4215-95e6-26a351ebc591 · outbound

This paper cites Towards the perceptual quality evaluation of compressed light field images,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Towards the perceptual quality evaluation of compressed light field images,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.574239Z

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Observation 1e2816dc-d737-4d11-b0ff-b41af58ed0e1 · outbound

This paper cites Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.557984Z

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Observation f9e7299e-9fd0-4a33-8735-2b32f7449ad1 · outbound

This paper cites Light field saliency detection with deep convolutional networks,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Light field saliency detection with deep convolutional networks,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.541018Z

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Observation caa484e1-c9e8-487c-a659-92081c82c1a9 · outbound

This paper cites an unresolved cited work.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:16:02.525072Z

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Observation 357c9773-ef7d-40d7-b042-948255e12489 · outbound

This paper cites Zwillinger and S.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions Zwillinger and S

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.509456Z

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Observation 2510502c-5876-4159-b29f-3ff2550c78dc · outbound

This paper cites On the Convergence of Adam and Beyond.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions On the Convergence of Adam and Beyond

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T19:16:02.291675Z

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Observation 1342d522-9c4a-473c-8cb3-643686f8ad8c · outbound

This paper cites No reference quality assessment of stereo video based on saliency and sparsity,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions No reference quality assessment of stereo video based on saliency and sparsity,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.493074Z

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Observation 4d1b4647-0f64-4c21-bb3e-61258927b12a · outbound

This paper cites JPEG Pleno: Standardizing a coding framework and tools for plenoptic imaging modalities,.

Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions JPEG Pleno: Standardizing a coding framework and tools for plenoptic imaging modalities,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T19:16:02.471711Z

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