No-Reference Image Quality Assessment
100 papers with code • 6 benchmarks • 6 datasets
An Image Quality Assessment approach where no reference image information is available to the model. Sometimes referred to as Blind Image Quality Assessment (BIQA).
Benchmarks
These leaderboards are used to track progress in No-Reference Image Quality Assessment
CSIQ
KADID-10k
MSU NR VQA Database
TID2013
UHD-IQA
Most implemented papers
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face image quality is an important factor to enable high performance face recognition systems.
RankIQA: Learning from Rankings for No-reference Image Quality Assessment
Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.
No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image.
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.
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
baidut/PaQ-2-PiQ
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CVPR 2020
Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts billions of viewers daily.
Task-Specific Normalization for Continual Learning of Blind Image Quality Models
zwx8981/tsn-iqa
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In this paper, we present a simple yet effective continual learning method for blind image quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/-length robustness.
Image Quality Assessment using Contrastive Learning
We consider the problem of obtaining image quality representations in a self-supervised manner.
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
iigroup/maniqa
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No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception.
QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution
Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images.
Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild
avinabsaha/ReIQA
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CVPR 2023
To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting.
