Geometry-driven distributed compression of the plenoptic function: performance bounds and constructive algorithms.
In this paper, we study the sampling and the distributed compression of the data acquired by a camera sensor network. The effective design of these sampling and compression schemes requires, however, the understanding of the structure of the acquired data. To this end, we show that the a priori knowledge of the configuration of the camera sensor network can lead to an effective estimation of such structure and to the design of effective distributed compression algorithms. For idealized scenarios, we derive the fundamental performance bounds of a camera sensor network and clarify the connection between sampling and distributed compression. We then present a distributed compression algorithm that takes advantage of the structure of the data and that outperforms independent compression algorithms on real multiview images.
Gehrig
Nicolas
N
Communications and Signal Processing Group, Electrical and Electronic Engineering, Imperial College London, London, U.K. nicolas.gehrig03@imperial.ac.uk
Dragotti
Pier Luigi
PL
D000465:Algorithms; D003195:Computer Communication Networks; D044962:Data Compression / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014741:Video Recording / Q000379:methods*