Bridge the Gap between Visual Difference Prediction Model and Just Noticeable Difference Subjective Datasets

In video compression applications, the term 75%SUR (Satisfied User Ratio) is used to describe the compression parameter with which only 75% of the users can not notice the difference between one compressed media and its source. 75%SUR is widely used in JND (Just Noticeable Difference) modeling as a common threshold to standardize differences in users' perceptions of JND location. Visible difference detection is an essential step in JND prediction. However, Visible Difference Predictors (VDP), as objective quality metrics, are usually calibrated and applied on media quality datasets, no study has yet trained or applied the VDP on JND datasets. In this work, we will explore the feasibility of using the VDP model in predicting SUR and JND. We focus on Video Wise JND(VW-JND) and propose the model Extend-FvVDP, which maps the continuous quality scores output from the current best-performing VDP model, the FovVideoVDP, to VW-JND ground truth. Finally, Extend-FvVDP got a mean SUR prediction error of 0.0624, a mean JND prediction error of 1.9318. Our results show that VDP still performs on the JND datasets, and the JND prediction using VDP has the potential to exceed that of pure deep learning models