Name
Abstract | ||
|---|---|---|
Blind quality assessment of screen content images (SCIs) is much challenging than traditional natural images. In this paper, we propose a blind quality predictor for SCIs to explore the issue from the perspective of sparse representation. Specifically, we conduct local sparse representation for the textual and pictorial regions, respectively, and conduct global sparse representation for the global... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TSMC.2017.2676180 | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
Keywords | Field | DocType |
Feature extraction,Image quality,Distortion,Training,Visualization,Distortion measurement,Degradation | Pattern recognition,Visualization,Computer science,Pooling,Sparse approximation,Image quality,Feature extraction,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
48 | 9 | 2168-2216 |
Citations | PageRank | References |
8 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Shao | 1 | 603 | 72.75 |
| Ying Gao | 2 | 42 | 8.50 |
| Fucui Li | 3 | 30 | 3.89 |
| Gangyi Jiang | 4 | 865 | 105.98 |