Name
Abstract | ||
|---|---|---|
Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIP.2017.2708503 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Electronics packaging,Training,Image quality,Predictive models,Feature extraction,Indexes | Learning to rank,Computer science,Image processing,Image quality,Digital image,Robustness (computer science),Artificial intelligence,Computer vision,Pattern recognition,Feature extraction,Ground truth,Pixel,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 8 | 1057-7149 |
Citations | PageRank | References |
33 | 0.78 | 62 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kede Ma | 1 | 773 | 27.93 |
| Wentao Liu | 2 | 110 | 14.31 |
| Tongliang Liu | 3 | 902 | 47.13 |
| Z Wang | 4 | 13331 | 630.91 |
| Dacheng Tao | 5 | 19032 | 747.78 |