Name
Abstract | ||
|---|---|---|
The great variations of videographic skills in videography, camera designs, compression and processing protocols, communication and bandwidth environments, and displays leads to an enormous variety of video impairments. Current no-reference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2018.2869673 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Streaming media,Quality assessment,Video recording,Databases,Distortion,Cameras,Image coding | Small number,Computer vision,Videography,Crowdsourcing,Subjective video quality,Bandwidth (signal processing),Perceptual video quality,Artificial intelligence,Video quality,Distortion,Multimedia,Mathematics | Journal |
Volume | Issue | ISSN |
28 | 2 | 1057-7149 |
Citations | PageRank | References |
20 | 0.72 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zeina Sinno | 1 | 28 | 3.52 |
| Alan C. Bovik | 2 | 5062 | 349.55 |