Name
Abstract | ||
|---|---|---|
Previously, no-reference (NR) stereoscopic 3D (S3D) image quality assessment (IQA) algorithms have been limited to the extraction of reliable hand-crafted features based on an understanding of the insufficiently revealed human visual system or natural scene statistics. Furthermore, compared with full-reference (FR) S3D IQA metrics, it is difficult to achieve competitive quality score predictions u... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIP.2017.2725584 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Feature extraction,Image quality,Measurement,Two dimensional displays,Visualization,Three-dimensional displays,Machine learning | Data mining,Computer science,Convolutional neural network,Human visual system model,Image quality,Artificial intelligence,Deep learning,Computer vision,Quality Score,Pattern recognition,Feature extraction,Mean opinion score,Scene statistics | Journal |
Volume | Issue | ISSN |
26 | 10 | 1057-7149 |
Citations | PageRank | References |
14 | 0.64 | 55 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heeseok Oh | 1 | 16 | 1.67 |
| Sewoong Ahn | 2 | 20 | 4.49 |
| Kim, J. | 3 | 127 | 8.56 |
| Sanghoon Lee | 4 | 740 | 97.47 |