  Accurate prediction of local distortion visibility thresholds is critical in
many image and video processing applications. Existing methods require an
accurate modeling of the human visual system, and are derived through
pshycophysical experiments with simple, artificial stimuli. These approaches,
however, are difficult to generalize to natural images with complex types of
distortion. In this paper, we explore a different perspective, and we
investigate whether it is possible to learn local distortion visibility from
image quality scores. We propose a convolutional neural network based
optimization framework to infer local detection thresholds in a distorted
image. Our model is trained on multiple quality datasets, and the results are
correlated with empirical visibility thresholds collected on complex stimuli in
a recent study. Our results are comparable to state-of-the-art mathematical
models that were trained on phsycovisual data directly. This suggests that it
is possible to predict psychophysical phenomena from visibility information
embedded in image quality scores.
