Name
Abstract | ||
|---|---|---|
The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IPTA.2017.8310083 | 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) |
Keywords | Field | DocType |
Lossless compression,image segmentation,plenoptic image,quantum cut segmentation | Computer vision,Pattern recognition,Ranking,Computer science,Segmentation,Image segmentation,Context model,Prediction algorithms,Artificial intelligence,Scaling,Detector,Lossless compression | Conference |
ISSN | ISBN | Citations |
2154-512X | 978-1-5386-1843-1 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| I. Schiopu | 1 | 37 | 8.04 |
| Moncef Gabbouj | 2 | 3282 | 386.30 |
| Alexandros Iosifidis | 3 | 841 | 72.43 |
| B Zeng | 4 | 1374 | 159.35 |
| Shuaicheng Liu | 5 | 363 | 28.26 |