  Image Quality Assessment (IQA) is essential in various Computer Vision tasks
such as image deblurring and super-resolution. However, most IQA methods
require reference images, which are not always available. While there are some
reference-free IQA metrics, they have limitations in simulating human
perception and discerning subtle image quality variations. We hypothesize that
the JPEG quality factor is representatives of image quality measurement, and a
well-trained neural network can learn to accurately evaluate image quality
without requiring a clean reference, as it can recognize image degradation
artifacts based on prior knowledge. Thus, we developed a reference-free quality
evaluation network, dubbed "Quality Factor (QF) Predictor", which does not
require any reference. Our QF Predictor is a lightweight, fully convolutional
network comprising seven layers. The model is trained in a self-supervised
manner: it receives JPEG compressed image patch with a random QF as input, is
trained to accurately predict the corresponding QF. We demonstrate the
versatility of the model by applying it to various tasks. First, our QF
Predictor can generalize to measure the severity of various image artifacts,
such as Gaussian Blur and Gaussian noise. Second, we show that the QF Predictor
can be trained to predict the undersampling rate of images reconstructed from
Magnetic Resonance Imaging (MRI) data.
