相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。MUSIQ: Multi-scale Image Quality Transformer
Junjie Ke et al.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021) (2021)
Saliency-Guided Transformer Network combined with Local Embedding for No-Reference Image Quality Assessment
Mengmeng Zhu et al.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) (2021)
Perceptual Image Quality Assessment with Transformers
Manri Cheon et al.
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021 (2021)
Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network
Weixia Zhang et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2020)
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment
Vlad Hosu et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)
Deep CNN-Based Blind Image Quality Predictor
Jongyoo Kim et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?
Dingquan Li et al.
IEEE TRANSACTIONS ON MULTIMEDIA (2019)
Blind image quality assessment via learnable attention-based pooling
Jie Gu et al.
PATTERN RECOGNITION (2019)
Naturalness-Aware Deep No-Reference Image Quality Assessment
Bo Yan et al.
IEEE TRANSACTIONS ON MULTIMEDIA (2019)
SGDNet: An End-to-End Saliency-Guided Deep Neural Network for No-Reference Image Quality Assessment
Sheng Yang et al.
PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19) (2019)
NIMA: Neural Image Assessment
Hossein Talebi et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Sebastian Bosse et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)
Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning
Kwan-Yee Lin et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)
Blind Predicting Similar Quality Map for Image Quality Assessment
Da Pan et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)
Fully Deep Blind Image Quality Predictor
Jongyoo Kim et al.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (2017)
Waterloo Exploration Database: New Challenges for Image Quality Assessment Models
Kede Ma et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2017)
Mask R-CNN
Kaiming He et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)
YFCC100M: The New Data in Multimedia Research
Bart Thomee et al.
COMMUNICATIONS OF THE ACM (2016)
Blind Image Quality Assessment Based on High Order Statistics Aggregation
Jingtao Xu et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2016)
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Deepti Ghadiyaram et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2016)
Group MAD Competition - A New Methodology to Compare Objective Image Quality Models
Kede Ma et al.
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2016)
A Feature-Enriched Completely Blind Image Quality Evaluator
Lin Zhang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2015)
VSI: A Visual Saliency-Induced Index for Perceptual Image Quality Assessment
Lin Zhang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2014)
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
Wufeng Xue et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2014)
No-Reference Image Quality Assessment in the Spatial Domain
Anish Mittal et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2012)
FSIM: A Feature Similarity Index for Image Quality Assessment
Lin Zhang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2011)
A statistical evaluation of recent full reference image quality assessment algorithms
Hamid Rahim Sheikh et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2006)
Image quality assessment: From error visibility to structural similarity
Z Wang et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2004)