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Quality-aware Pretrained Models for Blind Image Quality Assessment

https://doi.org/10.1109/cvpr52729.2023.02136

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-58621-8_45openalex-snapshot

Contrastive Multiview Coding

inferredcites →https://doi.org/10.1007/978-3-319-46487-9_40openalex-snapshot

Colorful Image Colorization

inferredcites →https://doi.org/10.1007/s11760-009-0117-4openalex-snapshot

No-reference assessment of blur and noise impacts on image quality

inferredcites →https://doi.org/10.1016/j.image.2016.08.008openalex-snapshot

CSV: Image quality assessment based on color, structure, and visual system

inferredcites →https://doi.org/10.1109/cvpr.2005.202openalex-snapshot

Learning a Similarity Metric Discriminatively, with Application to Face Verification

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2012.6247789openalex-snapshot

Unsupervised feature learning framework for no-reference image quality assessment

inferredcites →https://doi.org/10.1109/cvpr.2014.224openalex-snapshot

Convolutional Neural Networks for No-Reference Image Quality Assessment

inferredcites →https://doi.org/10.1109/cvpr.2015.7298682openalex-snapshot

FaceNet: A unified embedding for face recognition and clustering

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.213openalex-snapshot

Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework

inferredcites →https://doi.org/10.1109/cvpr.2017.76openalex-snapshot

Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction

inferredcites →https://doi.org/10.1109/cvpr.2018.00068openalex-snapshot

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

inferredcites →https://doi.org/10.1109/cvpr.2018.00393openalex-snapshot

Unsupervised Feature Learning via Non-parametric Instance Discrimination

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00363openalex-snapshot

From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00372openalex-snapshot

Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00373openalex-snapshot

Perceptual Quality Assessment of Smartphone Photography

inferredcites →https://doi.org/10.1109/cvpr42600.2020.00975openalex-snapshot

Momentum Contrast for Unsupervised Visual Representation Learning

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01415openalex-snapshot

MetaIQA: Deep Meta-Learning for No-Reference Image Quality Assessment

inferredcites →https://doi.org/10.1109/cvpr46437.2021.01323openalex-snapshot

Rich features for perceptual quality assessment of UGC videos

inferredcites →https://doi.org/10.1109/cvpr52688.2022.00943openalex-snapshot

SimMIM: a Simple Framework for Masked Image Modeling

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01553openalex-snapshot

Masked Autoencoders Are Scalable Vision Learners

inferredcites →https://doi.org/10.1109/cvprw53098.2021.00044openalex-snapshot

Deep Learning-based Distortion Sensitivity Prediction for Full-Reference Image Quality Assessment

inferredcites →https://doi.org/10.1109/cvprw56347.2022.00126openalex-snapshot

MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

inferredcites →https://doi.org/10.1109/iccv.2015.167openalex-snapshot

Unsupervised Visual Representation Learning by Context Prediction

inferredcites →https://doi.org/10.1109/iccv.2017.118openalex-snapshot

RankIQA: Learning from Rankings for No-Reference Image Quality Assessment

inferredcites →https://doi.org/10.1109/iccv.2017.97openalex-snapshot

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

inferredcites →https://doi.org/10.1109/iccv48922.2021.00475openalex-snapshot

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

inferredcites →https://doi.org/10.1109/iccv48922.2021.00510openalex-snapshot

MUSIQ: Multi-scale Image Quality Transformer

inferredcites →https://doi.org/10.1109/iccv48922.2021.00950openalex-snapshot

An Empirical Study of Training Self-Supervised Vision Transformers

inferredcites →https://doi.org/10.1109/iccvw54120.2021.00217openalex-snapshot

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

inferredcites →https://doi.org/10.1109/iccvw54120.2021.00222openalex-snapshot

Saliency-Guided Transformer Network combined with Local Embedding for No-Reference Image Quality Assessment

inferredcites →https://doi.org/10.1109/icip.2002.1038064openalex-snapshot

No-reference perceptual quality assessment of JPEG compressed images

inferredcites →https://doi.org/10.1109/icip.2018.8451285openalex-snapshot

Blind Image Quality Assessment with a Probabilistic Quality Representation

inferredcites →https://doi.org/10.1109/icip.2019.8803047openalex-snapshot

A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics

inferredcites →https://doi.org/10.1109/icip.2019.8803390openalex-snapshot

Blind Image Quality Assessment by Learning from Multiple Annotators

inferredcites →https://doi.org/10.1109/icip42928.2021.9506075openalex-snapshot

Transformer For Image Quality Assessment

inferredcites →https://doi.org/10.1109/icme.2012.109openalex-snapshot

Exploiting Image Local and Nonlocal Consistency for Mixed Gaussian-Impulse Noise Removal

inferredcites →https://doi.org/10.1109/lsp.2012.2227726openalex-snapshot

Making a “Completely Blind” Image Quality Analyzer

inferredcites →https://doi.org/10.1109/msp.2017.2736018openalex-snapshot

Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment

inferredcites →https://doi.org/10.1109/tbc.2018.2816783openalex-snapshot

Blind Image Quality Estimation via Distortion Aggravation

inferredcites →https://doi.org/10.1109/tcsvt.2018.2886771openalex-snapshot

Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network

inferredcites →https://doi.org/10.1109/tip.2005.854492openalex-snapshot

No-reference quality assessment using natural scene statistics: JPEG2000

inferredcites →https://doi.org/10.1109/tip.2010.2053549openalex-snapshot

No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers

inferredcites →https://doi.org/10.1109/tip.2011.2108661openalex-snapshot

Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images

inferredcites →https://doi.org/10.1109/tip.2011.2147325openalex-snapshot

Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality

inferredcites →https://doi.org/10.1109/tip.2012.2190086openalex-snapshot

No-Reference Image Quality Assessment Using Visual Codebooks

inferredcites →https://doi.org/10.1109/tip.2012.2191563openalex-snapshot

Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain

inferredcites →https://doi.org/10.1109/tip.2012.2197011openalex-snapshot

Reduced-Reference Image Quality Assessment by Structural Similarity Estimation

inferredcites →https://doi.org/10.1109/tip.2012.2214050openalex-snapshot

No-Reference Image Quality Assessment in the Spatial Domain

inferredcites →https://doi.org/10.1109/tip.2015.2426416openalex-snapshot

A Feature-Enriched Completely Blind Image Quality Evaluator

inferredcites →https://doi.org/10.1109/tip.2015.2500021openalex-snapshot

Massive Online Crowdsourced Study of Subjective and Objective Picture Quality

inferredcites →https://doi.org/10.1109/tip.2016.2601821openalex-snapshot

Reduced-Reference Quality Assessment Based on the Entropy of DWT Coefficients of Locally Weighted Gradient Magnitudes

inferredcites →https://doi.org/10.1109/tip.2016.2631888openalex-snapshot

Waterloo Exploration Database: New Challenges for Image Quality Assessment Models

inferredcites →https://doi.org/10.1109/tip.2017.2708503openalex-snapshot

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

inferredcites →https://doi.org/10.1109/tip.2017.2760518openalex-snapshot

Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment

inferredcites →https://doi.org/10.1109/tip.2017.2774045openalex-snapshot

End-to-End Blind Image Quality Assessment Using Deep Neural Networks

inferredcites →https://doi.org/10.1109/tip.2018.2875913openalex-snapshot

Combining Local and Global Measures for DIBR-Synthesized Image Quality Evaluation

inferredcites →https://doi.org/10.1109/tip.2020.2967829openalex-snapshot

KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment

inferredcites →https://doi.org/10.1109/tip.2020.3002478openalex-snapshot

End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network

inferredcites →https://doi.org/10.1109/tip.2021.3061932openalex-snapshot

Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild

inferredcites →https://doi.org/10.1109/tip.2021.3072221openalex-snapshot

UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content

inferredcites →https://doi.org/10.1109/tip.2021.3130536openalex-snapshot

Contrastive Self-Supervised Pre-Training for Video Quality Assessment

inferredcites →https://doi.org/10.1109/tip.2022.3181496openalex-snapshot

Image Quality Assessment Using Contrastive Learning

inferredcites →https://doi.org/10.1109/tmm.2018.2875354openalex-snapshot

Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?

inferredcites →https://doi.org/10.1109/tnnls.2017.2649101openalex-snapshot

Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

inferredcites →https://doi.org/10.1109/wacv51458.2022.00404openalex-snapshot

No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency

inferredcites →https://doi.org/10.1145/1390156.1390294openalex-snapshot

Extracting and composing robust features with denoising autoencoders

inferredcites →https://doi.org/10.1145/3343031.3351028openalex-snapshot

Quality Assessment of In-the-Wild Videos

inferredcites →https://doi.org/10.1145/3394171.3413804openalex-snapshot

Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment

inferredcites →https://doi.org/10.1145/3503161.3547872openalex-snapshot

Image Quality Assessment: From Mean Opinion Score to Opinion Score Distribution

inferredcites →https://doi.org/10.1146/annurev.neuro.24.1.1193openalex-snapshot

Natural Image Statistics and Neural Representation

inferredcites →https://doi.org/10.3390/e24040551openalex-snapshot

Survey on Self-Supervised Learning: Auxiliary Pretext Tasks and Contrastive Learning Methods in Imaging

inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot

Exploiting Generative AI to Scale up Intelligent Tutoring Systems

inferredcites →https://doi.org/10.48550/arxiv.1807.03748openalex-snapshot

Representation Learning with Contrastive Predictive Coding

inferredcites →https://doi.org/10.48550/arxiv.2003.04297openalex-snapshot

Improved Baselines with Momentum Contrastive Learning

inferredcites →https://doi.org/10.48550/arxiv.2206.02680openalex-snapshot

Separable Self-attention for Mobile Vision Transformers

inferredcites →https://doi.org/10.5555/1756006.1953039openalex-snapshot

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot

Learning Multiple Layers of Features from Tiny Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality Assessment

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PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts

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XPSR: Cross-Modal Priors for Diffusion-Based Image Super-Resolution

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RMT-BVQA: Recurrent Memory Transformer Based Blind Video Quality Assessment for Enhanced Video Content

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MobileIQA: Exploiting Mobile-Level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation

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Assessing UHD Image Quality from Aesthetics, Distortions, and Saliency

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Degradation-aware contrastive learning with frequency-spatial fusion: a two-stage framework for no-reference image quality assessment

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Perceptually Guided Gaussian Splatting (PGGS): hyperparameter control with perceptual rewards and view augmentation

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Visual quality assessment of E-commerce product images using convolutional neural networks

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Hierarchical Curriculum Learning for No-Reference Image Quality Assessment

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SCoRE: Standardized Human Evaluation Provides a Reliable Measure for Semantic Consistency of Text-to-Image Generation

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Image Quality Difference Perception Ability: A BIQA model effectiveness metric based on model falsification method

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Quality Assessment of Low-Light Restored Images: A Subjective Study and an Unsupervised Model

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DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer

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CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.01216openalex-snapshot

Blind Image Quality Assessment Based on Geometric Order Learning

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.02451openalex-snapshot

Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment

inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.02453openalex-snapshot

KVQ: Kwai Video Quality Assessment for Short-form Videos

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inferred← citeshttps://doi.org/10.1109/wacv57701.2024.00425openalex-snapshot

VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data

inferred← citeshttps://doi.org/10.1109/wacvw65960.2025.00027openalex-snapshot

A Distortion Aware Image Quality Assessment Model

inferred← citeshttps://doi.org/10.1111/exsy.13842openalex-snapshot

No‐Reference Image Quality Assessment: Past, Present, and Future

inferred← citeshttps://doi.org/10.1360/ssi-2024-0394openalex-snapshot

A review of fine-grained object perception in extreme space environments

inferred← citeshttps://doi.org/10.1371/journal.pone.0328227openalex-snapshot

Synergistic fusion: An integrated pipeline of CLAHE, YOLO models, and advanced super-resolution for enhanced thermal eye detection

inferred← citeshttps://doi.org/10.1609/aaai.v38i4.28170openalex-snapshot

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment

inferred← citeshttps://doi.org/10.1631/fitee.2400904openalex-snapshot

Image generation evaluation: a comprehensive survey of human and automatic evaluations

inferred← citeshttps://doi.org/10.2139/ssrn.4622422openalex-snapshot

A Deep-DiD Method to Estimate Heterogeneous Treatment Effects: Application to Content Creator Selection

inferred← citeshttps://doi.org/10.2139/ssrn.5272649openalex-snapshot

Improving Image Aesthetic Assessment Via Efficient Self-Training

inferred← citeshttps://doi.org/10.2139/ssrn.5349638openalex-snapshot

Fusion-Based Blind Image Quality Assessment for Wild Images

inferred← citeshttps://doi.org/10.32628/ijsrst52310626openalex-snapshot

A Literature Survey on Completely Blind Image Quality Evaluator Feature-Enriched

inferred← citeshttps://doi.org/10.32628/ijsrst52310686openalex-snapshot

A Literature Survey on Completely Blind Image Quality Evaluator Feature-Enriched

inferred← citeshttps://doi.org/10.3390/app15041996openalex-snapshot

ISAR Image Quality Assessment Based on Visual Attention Model

inferred← citeshttps://doi.org/10.3390/math12081185openalex-snapshot

McmIQA: Multi-Module Collaborative Model for No-Reference Image Quality Assessment

inferred← citeshttps://doi.org/10.7717/peerj-cs.2654openalex-snapshot

A quality assessment algorithm for no-reference images based on transfer learning

inferred← citeshttps://doi.org/10.7717/peerj-cs.4093openalex-snapshot

A Swin transformer-based framework for digital media image quality assessment

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