From Just Noticeable Differences to Image Quality

Distortions can occur due to several processing steps in the imaging chain of a wide range of multimedia content. The visibility of distortions is highly correlated with the overall perceived quality of a certain multimedia content. Subjective quality evaluation of images relies mainly on mean opinion scores (MOS) to provide ground-truth for measuring image quality on a continuous scale. Alternatively, just noticeable difference (JND) defines the visibility of distortions as a binary measurement based on an anchor point. By using the pristine reference as the anchor, the first JND point can be determined. This first JND point provides an intrinsic quantification of the visible distortions within the multimedia content. Therefore, it is intuitively appealing to develop a quality assessment model by utilizing the JND information as the fundamental cornerstone. In this work, we use the first JND point information to train a Siamese Convolutional Neural Network to predict image quality scores on a continuous scale. To ensure generalization, we incorporated a white-box optical retinal pathway model to acquire achromatic responses. The proposed model, D-JNDQ, displays a competitive performance on cross dataset evaluation conducted on TID2013 dataset, proving the generalization of the model on unseen distortion types and supra-threshold distortion levels.