Image Quality Estimation
13 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Image Quality Estimation
Most implemented papers
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
sheqi/GAN_Review
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While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision.
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.
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation
Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i. e. \emph{domain adaptation}.
UNIQUE: Unsupervised Image Quality Estimation
A linear decoder is trained with 7 GB worth of data, which corresponds to 100, 000 8x8 image patches randomly obtained from nearly 1, 000 images in the ImageNet 2013 database.
MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation
We use multiple linear decoders to capture different abstraction levels of the image patches.
Image Quality Assessment using Contrastive Learning
We consider the problem of obtaining image quality representations in a self-supervised manner.
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.
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.
Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies
Apathetically/ProxyOpt
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SIGGRAPH 2019 2019
We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i. e., application-specific) metric.
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.
