Image Quality Estimation
13 papers with code • 0 benchmarks • 0 datasets
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Latest papers
Saliency-Guided Local Full-Reference Image Quality Assessment
In addition to this, visual saliency was utilized as weights in the weighted averaging of local image quality scores, emphasizing image regions that are salient to human observers.
3D Saliency guided Deep Quality predictor for No-Reference Stereoscopic Images
The use of 3D technologies is growing rapidly, and stereoscopic imaging is usually used to display the 3D contents.
Image Quality Assessment using Contrastive Learning
We consider the problem of obtaining image quality representations in a self-supervised manner.
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.
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.
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.
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
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.
Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies
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.
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.
Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.