Blind Image Quality Assessment
39 papers with code • 0 benchmarks • 2 datasets
See No-Reference Image Quality Assessment (NR-IQA).
Benchmarks
These leaderboards are used to track progress in Blind Image Quality Assessment
MSU NR VQA Database
PIQ23
Most implemented papers
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.
Adaboost Neural Network And Cyclopean View For No-reference Stereoscopic Image Quality Assessment
The benchmark LIVE 3D phase-I, phase-II, and IRCCyN/IVC 3D databases have been used to evaluate the performance of the proposed approach.
A CNN-Based Blind Denoising Method for Endoscopic Images
ShaofengZou/A-CNN-Based-Blind-Denoising-Method
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Conference 2019
The number of iterations is reduced about 36% by using transfer learning in our DIP process.
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).
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.
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.
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.
Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric
lyp22/ResSCNN
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Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years.
No-reference Stereoscopic Image Quality Predictor using Deep Features from Cyclopean Image
Taking this into account, this paper introduces a blind stereoscopic image quality measurement using synthesized cyclopean image and deep feature extraction.
Continual Learning for Blind Image Quality Assessment
zwx8981/BIQA_CL
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In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data.
