Blind Image Quality Assessment
12 papers with code • 0 benchmarks • 0 datasets
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Greatest papers with code
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions.
Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network
The vast majority of prior BIQA methods focus on how to predict synthetic image quality, but fail when applied to real-world distorted images.
A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction
Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model.
Perceptual Quality Assessment of Smartphone Photography
h4nwei/SPAQ
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CVPR 2020
As smartphones become people's primary cameras to take photos, the quality of their cameras and the associated computational photography modules has become a de facto standard in evaluating and ranking smartphones in the consumer market.
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets.
Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images
lidq92/SFA
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To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
Which Has Better Visual Quality: The Clear Blue Sky or a Blurry Animal?
lidq92/SFA
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IEEE Transactions on Multimedia 2018
The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE.
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
zwx8981/UNIQUE
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Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthetic distortions remain particularly weak at handling realistic distortions (and vice versa).
Learning to Blindly Assess Image Quality in the Laboratory and Wild
zwx8981/UNIQUE
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Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images.
Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment
Experiments on two relevant datasets (KonIQ-10k and CLIVE) show that, compared to MAE or MSE loss, the new loss enables the IQA model to converge about 10 times faster and the final model achieves better performance.
Blind Image Quality Assessment
No-Reference Image Quality Assessment