Real time multi-view light field image coding using sparse representation
| Project/Area Number |
15K00257
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Perceptual information processing
|
|---|
| Research Institution | Kurume National College of Technology |
|---|
Principal Investigator |
Kuroki Yoshimitsu 久留米工業高等専門学校, 制御情報工学科, 教授 (60290847)
|
|---|
| Project Period (FY) |
2015-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Keywords | ライトフィールド画像 / 分散圧縮符号化 / 多視点画像処理 / 信号のスパース表現 / 凸最適化問題 / エピポーラ幾何 |
|---|
| Outline of Final Research Achievements |
The purpose of this research is to develop real time encoding of multi-view images captured by light field cameras. This camera itself can capture multi-view images. By employing the distributed sparse coding framework, which is different from international standards, we achieve the purpose. The encoder of this framework is very simple: the encoder just multiplies given images by a low-rank random matrix. The decoder estimates the original images on the assumption of the images' sparseness. This framework decreases energy consumption of encoders, and powerful server machines on "cloud" decode the images. Then, this scheme contributes to IoT environment. This research designs the sparse dictionaries as a convex optimization problem, and improves reconstructed image quality and efficiency by applying TV-l1 for disparity prediction. This work also developed accurate estimation of the fundamental matrix of the epipolar geometry.
|
Report
(4 results)
Research Products
(19 results)
