Analysis and prediction of JND-based video quality model
This work addresses video quality prediction for compressed video applications, but appears incremental as it builds on existing JND and SUR concepts with specific modeling improvements.
The authors tackled video quality assessment by modeling just-noticeable-difference (JND) points using a normal distribution and predicting satisfied user ratio (SUR) curves with quality degradation and masking features. Their method achieved a mean absolute error smaller than 0.05 for SUR across various resolutions.
The just-noticeable-difference (JND) visual perception property has received much attention in characterizing human subjective viewing experience of compressed video. In this work, we quantify the JND-based video quality assessment model using the satisfied user ratio (SUR) curve, and show that the SUR model can be greatly simplified since the JND points of multiple subjects for the same content in the VideoSet can be well modeled by the normal distribution. Then, we design an SUR prediction method with video quality degradation features and masking features and use them to predict the first, second and the third JND points and their corresponding SUR curves. Finally, we verify the performance of the proposed SUR prediction method with different configurations on the VideoSet. The experimental results demonstrate that the proposed SUR prediction method achieves good performance in various resolutions with the mean absolute error (MAE) of the SUR smaller than 0.05 on average.