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Blind Image Quality Index with High-Level Semantic Guidance and Low-Level Fine-Grained Representation

https://doi.org/10.2139/ssrn.4715281
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CBAM: Convolutional Block Attention Module
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_18openalex-extracted
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
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PVT v2: Improved baselines with pyramid vision transformer
inferredcites →https://doi.org/10.1016/j.image.2014.06.007openalex-extracted
Reduced reference image quality assessment using regularity of phase congruency
inferredcites →https://doi.org/10.1016/j.ins.2020.12.018openalex-extracted
Blind image quality prediction with hierarchical feature aggregation
inferredcites →https://doi.org/10.1016/j.neucom.2018.11.015openalex-extracted
Blind image quality assessment based on joint log-contrast statistics
inferredcites →https://doi.org/10.1016/j.neucom.2020.12.009openalex-extracted
Deep blind image quality assessment based on multiple instance regression
inferredcites →https://doi.org/10.1016/j.neucom.2021.12.098openalex-extracted
ASSP: An adaptive sample statistics-based pooling for full-reference image quality assessment
inferredcites →https://doi.org/10.1016/j.neucom.2022.05.043openalex-extracted
Blind image quality assessment based on progressive multi-task learning
inferredcites →https://doi.org/10.1016/j.neucom.2023.01.067openalex-extracted
Efficient data-driven behavior identification based on vision transformers for human activity understanding
inferredcites →https://doi.org/10.1016/j.sigpro.2022.108782openalex-extracted
No-reference quality index of tone-mapped images based on authenticity, preservation, and scene expressiveness
inferredcites →https://doi.org/10.1109/cvpr.2012.6247789openalex-extracted
Unsupervised feature learning framework for no-reference image quality assessment
inferredcites →https://doi.org/10.1109/cvpr.2012.6247954openalex-extracted
AVA: A large-scale database for aesthetic visual analysis
inferredcites →https://doi.org/10.1109/cvpr.2018.00083openalex-extracted
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning
inferredcites →https://doi.org/10.1109/cvpr.2019.00960openalex-extracted
Effective Aesthetics Prediction With Multi-Level Spatially Pooled Features
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00372openalex-extracted
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01412openalex-extracted
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01716openalex-extracted
Uformer: A General U-Shaped Transformer for Image Restoration
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01924openalex-extracted
Personalized Image Aesthetics Assessment with Rich Attributes
inferredcites →https://doi.org/10.1109/cvprw53098.2021.00027openalex-extracted
HINet: Half Instance Normalization Network for Image Restoration
inferredcites →https://doi.org/10.1109/iccv.2017.118openalex-extracted
RankIQA: Learning from Rankings for No-Reference Image Quality Assessment
inferredcites →https://doi.org/10.1109/lsp.2012.2227726openalex-extracted
Making a “Completely Blind” Image Quality Analyzer
inferredcites →https://doi.org/10.1109/tbc.2022.3215249openalex-extracted
An End-to-End Blind Image Quality Assessment Method Using a Recurrent Network and Self-Attention
inferredcites →https://doi.org/10.1109/tbc.2023.3262163openalex-extracted
Multilevel Feature Fusion for End-to-End Blind Image Quality Assessment
inferredcites →https://doi.org/10.1109/tcsvt.2018.2886771openalex-extracted
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network
inferredcites →https://doi.org/10.1109/tcsvt.2021.3055197openalex-extracted
MMMNet: An End-to-End Multi-Task Deep Convolution Neural Network With Multi-Scale and Multi-Hierarchy Fusion for Blind Image Quality Assessment
inferredcites →https://doi.org/10.1109/tcsvt.2021.3096528openalex-extracted
Fine-Grained Image Quality Assessment: A Revisit and Further Thinking
inferredcites →https://doi.org/10.1109/tcsvt.2021.3112197openalex-extracted
Blind Image Quality Index for Authentic Distortions With Local and Global Deep Feature Aggregation
inferredcites →https://doi.org/10.1109/tcsvt.2022.3186307openalex-extracted
Distilling Knowledge From Object Classification to Aesthetics Assessment
inferredcites →https://doi.org/10.1109/tcsvt.2023.3249185openalex-extracted
Theme-Aware Visual Attribute Reasoning for Image Aesthetics Assessment
inferredcites →https://doi.org/10.1109/tcyb.2022.3169017openalex-extracted
MetaMP: Metalearning-Based Multipatch Image Aesthetics Assessment
inferredcites →https://doi.org/10.1109/tip.2010.2053549openalex-extracted
No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers
inferredcites →https://doi.org/10.1109/tip.2011.2147325openalex-extracted
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
inferredcites →https://doi.org/10.1109/tip.2012.2214050openalex-extracted
No-Reference Image Quality Assessment in the Spatial Domain
inferredcites →https://doi.org/10.1109/tip.2016.2585880openalex-extracted
Blind Image Quality Assessment Based on High Order Statistics Aggregation
inferredcites →https://doi.org/10.1109/tip.2017.2760518openalex-extracted
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
inferredcites →https://doi.org/10.1109/tip.2017.2774045openalex-extracted
End-to-End Blind Image Quality Assessment Using Deep Neural Networks
inferredcites →https://doi.org/10.1109/tip.2018.2831899openalex-extracted
NIMA: Neural Image Assessment
inferredcites →https://doi.org/10.1109/tip.2019.2941778openalex-extracted
A Unified Probabilistic Formulation of Image Aesthetic Assessment
inferredcites →https://doi.org/10.1109/tip.2020.2967829openalex-extracted
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment
inferredcites →https://doi.org/10.1109/tip.2020.2968285openalex-extracted
Personality-Assisted Multi-Task Learning for Generic and Personalized Image Aesthetics Assessment
inferredcites →https://doi.org/10.1109/tip.2022.3144892openalex-extracted
VCRNet: Visual Compensation Restoration Network for No-Reference Image Quality Assessment
inferredcites →https://doi.org/10.1109/tmm.2023.3243683openalex-extracted
Visual Interaction Perceptual Network for Blind Image Quality Assessment
inferredcites →https://doi.org/10.1109/tnnls.2018.2829819openalex-extracted
Deep CNN-Based Blind Image Quality Predictor
inferredcites →https://doi.org/10.1146/annurev.ne.16.030193.002101openalex-extracted
How Parallel are the Primate Visual Pathways?
inferredcites →https://doi.org/10.21203/rs.3.rs-970679/v1openalex-extracted
Deep Superpixel-Based Network For Blind Image Quality Assessment
inferredcites →https://doi.org/10.24963/ijcai.2022/132openalex-extracted
Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks
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