  Significant improvement has been made on just noticeable difference (JND)
modelling due to the development of deep neural networks, especially for the
recently developed unsupervised-JND generation models. However, they have a
major drawback that the generated JND is assessed in the real-world signal
domain instead of in the perceptual domain in the human brain. There is an
obvious difference when JND is assessed in such two domains since the visual
signal in the real world is encoded before it is delivered into the brain with
the human visual system (HVS). Hence, we propose an HVS-inspired signal
degradation network for JND estimation. To achieve this, we carefully analyze
the HVS perceptual process in JND subjective viewing to obtain relevant
insights, and then design an HVS-inspired signal degradation (HVS-SD) network
to represent the signal degradation in the HVS. On the one hand, the well
learnt HVS-SD enables us to assess the JND in the perceptual domain. On the
other hand, it provides more accurate prior information for better guiding JND
generation. Additionally, considering the requirement that reasonable JND
should not lead to visual attention shifting, a visual attention loss is
proposed to control JND generation. Experimental results demonstrate that the
proposed method achieves the SOTA performance for accurately estimating the
redundancy of the HVS. Source code will be available at
https://github.com/jianjin008/HVS-SD-JND.
