Image Quality Assessment
180 papers with code • 3 benchmarks • 10 datasets
We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models.
We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.
For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i. e., artifact-free).
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.
Face image quality is an important factor to enable high performance face recognition systems.
The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality.