Name
Abstract | ||
|---|---|---|
Video quality assessment plays an important role in video processing and communication applications. We propose a full reference video quality metric by combining a content-weighted spatial pooling strategy with a temporal pooling strategy. All pixels in a frame are classified into edge, texture, and smooth regions, and their structural similarity image index (SSIM) maps are divided into increasing and saturated regions by the curve of their SSIM values, then a content weight method is applied to increasing regions to get the score of an image frame. Finally, a temporal pooling method is used to get the overall video quality. Experimental results on the LIVE and IVP video quality databases show our proposed method works well in matching subjective scores. (C) 2015 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/1.JEI.24.5.053001 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
video quality assessment,content-weighted,temporal pooling,structural similarity image index | Computer vision,Video processing,Quality measurement,Pattern recognition,Image frame,Computer science,Pooling,Subjective video quality,Artificial intelligence,Pixel,Video quality | Journal |
Volume | Issue | ISSN |
24 | 5 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao-feng Li | 1 | 148 | 16.45 |
| Feng Pan | 2 | 0 | 0.68 |
| Xiaojun Wu | 3 | 356 | 52.89 |
| Yiwen Ju | 4 | 1 | 1.72 |
| Yun-Hao Yuan | 5 | 235 | 22.18 |
| Wei Fang | 6 | 81 | 7.96 |