| Session: | Image and Video Quality Assessment with Industry Applications |
| Location: | Lecture Room |
| Session Time: | Tuesday, June 26, 10:20 - 12:40 |
| Presentation Time: | Tuesday, June 26, 10:20 - 10:40 |
| Presentation: |
Special Session Lecture
|
| Paper Title: |
ANALYSIS AND PREDICTION OF JND-BASED VIDEO QUALITY MODEL |
| Authors: |
Haiqiang Wang; University of Southern California, United States | | |
| | Xinfeng Zhang; University of Southern California, United States | | |
| | Chao Yang; University of Southern California, United States | | |
| | C.-C. Jay Kuo; University of Southern California, United States | | |
| Abstract: |
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. |