| Session: | Poster Session |
| Location: | Poster Area |
| Session Time: | Wednesday, June 27, 15:40 - 17:00 |
| Presentation Time: | Wednesday, June 27, 15:40 - 16:40 |
| Presentation: |
Poster
|
| Paper Title: |
REGION-WISE SUPER-RESOLUTION ALGORITHM BASED ON THE VIEWPOINT DISTRIBUTION |
| Authors: |
Kazunori Uruma; Tokyo University of Science, Japan | | |
| | Shunsuke Takasu; Tokyo University of Science, Japan | | |
| | Keiko Masuda; Tokyo University of Science, Japan | | |
| | Seiichiro Hangai; Tokyo University of Science, Japan | | |
| Abstract: |
Recently, super-resolution techniques have been energetically studied for the purpose of reusing the low resolution image contents. Although a lot of approaches to achieve the appropriate super-resolution have been proposed such as non-linear filtering, total variation regularization, deep learning etc., the characteristic of the viewpoint distribution of the observer has not been effectively utilized. Because applying super-resolution to unimportant regions in an image may hinder the observer’s attention to seeing the display, it leads to a low subjective evaluation. This paper proposes the region-wise super-resolution algorithm based on the viewpoint distribution of observer. However, we cannot obtain the viewpoint distribution map for an image without the pre-experiment using the device such as eye mark recorder, therefore, the saliency map is utilized in this paper. Numerical examples show that the proposed algorithm using saliency map achieves a higher subjective evaluation than the previous study based on the non-linear filtering based super-resolution. Furthermore, in numerical examples, the proposed algorithm using the saliency map is shown to give the similar results of the algorithm using the viewpoint distribution map obtained by the pre-experiment using eye mark recorder. |