Name
Abstract | ||
|---|---|---|
The direct use of a deep convolutional neural network (CNN) in no-reference image quality assessment (NR-IQA) usually struggles for a good performance due to a lack of training data, which can be alleviated by transfer learning. However, depending on the similarity between the source and target tasks, the final performance differs vastly. In particular, various kinds of distortion types exist in IQA, which requires different kinds of features to predict visual quality. In this paper, to make the transferred model robust to various distortion types, we propose a Multiple-level Feature-based Image Quality Assessor (MFIQA) which considers multiple levels of features simultaneously. Through rigorous experiments, we prove that MFIQA consistently yields state-of-the-art performance regardless of the distortion types including synthetic and authentic corruption. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICIP.2018.8451346 | 2018 25th IEEE International Conference on Image Processing (ICIP) |
Keywords | Field | DocType |
Convolutional neural network,no-reference image quality assessment | Pattern recognition,Task analysis,Convolutional neural network,Computer science,Transfer of learning,Transform coding,Image quality,Feature extraction,Correlation,Artificial intelligence,Distortion | Conference |
ISSN | ISBN | Citations |
1522-4880 | 978-1-4799-7062-9 | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kim, J. | 1 | 127 | 8.56 |
| Anh-Duc Nguyen | 2 | 31 | 4.87 |
| Sewoong Ahn | 3 | 20 | 4.49 |
| chong luo | 4 | 696 | 47.36 |
| Sanghoon Lee | 5 | 740 | 97.47 |