Name
Abstract | ||
|---|---|---|
We address the problem of distributed compression in camera sensor networks. Our approach uses some geometrical information in order to estimate the correlation in the visual data. This correlation, which is related to the structure of the plenoptic function, can then be used to reduce the overall transmission rate from the sensors to a common central receiver. Our approach allows for a flexible allocation of the bit-rates amongst the encoders and can be made resilient to a fixed number of occlusions. Finally, we show that our distributed coding approach can be extended to general binary sources. The technique we propose uses linear channel codes and can achieve any point of the Slepian-Wolf achievable rate region. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/MMSP.2004.1436555 | MMSP |
Keywords | Field | DocType |
cameras,channel coding,correlation methods,data compression,hidden feature removal,image coding,wireless sensor networks,Slepian-Wolf achievable rate region,camera sensor network,common central receiver,distributed coding approach,distributed compression,flexible allocation,general binary source,linear channel code,plenoptic function,transmission rate | Computer vision,Computer science,Communication channel,Coding (social sciences),Artificial intelligence,Encoder,Data compression,Wireless sensor network,Image compression,Binary number,Wavelet transform | Conference |
ISBN | Citations | PageRank |
0-7803-8578-0 | 13 | 0.84 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicolas Gehrig | 1 | 65 | 5.40 |
| Dragotti, P.L. | 2 | 512 | 39.29 |