Name
Title | ||
|---|---|---|
Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions |
Abstract | ||
|---|---|---|
In multimedia broadcasting, no-reference image quality assessment (NR-IQA) is used to indicate the user-perceived quality of experience (QoE) and to support intelligent data transmission while optimizing user experience. This paper proposes an improved no-reference light field image quality assessment (NR-LFIQA) metric for future immersive media broadcasting services. First, we extend the concept ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TBC.2021.3099737 | IEEE Transactions on Broadcasting |
Keywords | DocType | Volume |
Feature extraction,Measurement,Image quality,Task analysis,Media,Visualization,Quality of experience | Journal | 67 |
Issue | ISSN | Citations |
4 | 0018-9316 | 4 |
PageRank | References | Authors |
0.41 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Qu | 1 | 4 | 0.41 |
| Xiaoming Chen | 2 | 50 | 4.50 |
| Vera Chung | 3 | 4 | 0.41 |
| Zhibo Chen | 4 | 4 | 1.08 |