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Accurate prediction of local distortion visibility thresholds is critical inmany image and video processing applications. Existing methods require anaccurate modeling of the human visual system, and are derived throughpshycophysical experiments with simple, artificial stimuli. These approaches,however, are difficult to generalize to natural images with complex types ofdistortion. In this paper, we explore a different perspective, and weinvestigate whether it is possible to learn local distortion visibility fromimage quality scores. We propose a convolutional neural network basedoptimization framework to infer local detection thresholds in a distortedimage. Our model is trained on multiple quality datasets, and the results arecorrelated with empirical visibility thresholds collected on complex stimuli ina recent study. Our results are comparable to state-of-the-art mathematicalmodels that were trained on phsycovisual data directly. This suggests that itis possible to predict psychophysical phenomena from visibility informationembedded in image quality scores.

Learning Local Distortion Visibility From Image Quality Data-sets