Name
Abstract | ||
|---|---|---|
The temporal flicker distortion is one of the most annoying noises in synthesized virtual view videos when they are rendered by compressed multi-view video plus depth in Three Dimensional (3D) video system. To assess the synthesized view video quality and further optimize the compression techniques in 3D video system, objective video quality assessment which can accurately measure the flicker distortion is highly needed. In this paper, we propose a full reference sparse representation-based video quality assessment method toward synthesized 3D videos. First, a synthesized video, treated as a 3D volume data with spatial (X-Y) and temporal (T) domains, is reformed and decomposed as a number of spatially neighboring temporal layers, i.e., X-T or Y-T planes. Gradient features in temporal layers of the synthesized video and strong edges of depth maps are used as key features in detecting the location of flicker distortions. Second, the dictionary learning and sparse representation for the temporal layers are then derived and applied to effectively represent the temporal flicker distortion. Third, a rank pooling method is used to pool all the temporal layer scores and obtain the score for the flicker distortion. Finally, the temporal flicker distortion measurement is combined with the conventional spatial distortion measurement to assess the quality of synthesized 3D videos. Experimental results on synthesized video quality database demonstrate our proposed method is significantly superior to the other state-of-the-art methods, especially on the view synthesis distortions induced from depth videos. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIP.2019.2929433 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
Keywords | Field | DocType |
Distortion,Three-dimensional displays,Distortion measurement,Quality assessment,Video recording,Feature extraction | Computer vision,Flicker,Pattern recognition,Pooling,Sparse approximation,Spatial distortion,Feature extraction,View synthesis,Artificial intelligence,Distortion,Video quality,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 1 | 1057-7149 |
Citations | PageRank | References |
5 | 0.39 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun Zhang | 1 | 497 | 24.67 |
| Huan Zhang | 2 | 104 | 20.09 |
| Mei Yu | 3 | 542 | 86.20 |
| Sam Kwong | 4 | 4590 | 315.78 |
| Yo-Sung Ho | 5 | 1288 | 146.57 |