Name
Title | ||
|---|---|---|
Deep Learning-based Distortion Sensitivity Prediction for Full-Reference Image Quality Assessment |
Abstract | ||
|---|---|---|
Previous full-reference image quality assessment methods aim to evaluate the quality of images impaired by traditional distortions such as JPEG, white noise, Gaussian blur, and so on. However, there is a lack of research measuring the quality of images generated by various image processing algorithms, including super-resolution, denoising, restoration, etc. Motivated by the previous model that pre... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPRW53098.2021.00044 | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | DocType | ISSN |
Image quality,Visualization,Sensitivity,Databases,Superresolution,Transform coding,Predictive models | Conference | 2160-7508 |
ISBN | Citations | PageRank |
978-1-6654-4899-4 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sewoong Ahn | 1 | 20 | 4.49 |
| Yeji Choi | 2 | 0 | 3.72 |
| Kwangjin Yoon | 3 | 24 | 3.91 |