Collaborative Data Compression Using Clustered Source Coding for Wireless Multimedia Sensor Networks

Abstract

Abstract-Data redundancy caused by correlation has motivated the application of collaborative multimedia in-network processing for data filtering and compression in wireless multimedia sensor networks (WMSNs). This paper proposes an information theoretic data compression framework with an objective to maximize the overall compression of the visual information gathered in a WMSN. To achieve this, an entropy-based divergence measure (EDM) scheme is proposed to predict the compression efficiency of performing joint coding on the images collected by spatially correlated cameras. The novelty of EDM relies on its independence of the specific image types and coding algorithms, thereby providing a generic mechanism for prior evaluation of compression under different coding solutions. Utilizing the predicted results from EDM, a distributed multi-cluster coding protocol (DMCP) is proposed to construct a compression-oriented coding hierarchy. The DMCP aims to partition the entire network into a set of coding clusters such that the global coding gain is maximized. Moreover, in order to enhance decoding reliability at data sink, the DMCP also guarantees that each sensor camera is covered by at least two different coding clusters. Experiments on H.264 standards show that the proposed EDM can effectively predict the joint coding efficiency from multiple sources. Further simulations demonstrate that the proposed compression framework can reduce 10% -23% total coding rate compared with the individual coding scheme, i.e., each camera sensor compresses its own image independently.

A Camera Nodes Correlation Model Based on 3D Sensing in Wireless Multimedia Sensor Networks

“…But the offset angle of camera, which expresses the area of camera's FoVs, is not investigated, especially, when the offset angle of two cameras is unequivalent. In [5], based on the spatial correlation model put forward in [4], an information-theoretic data compression framework is proposed with the objective to maximize the overall compression of the visual information retrieved by a WMSN.…”

“…Once the area of the FoV of camera and intersection area of the FoVs of two cameras are calculated, we can get the correlation coefficient ρ of camera C i and camera C j in (3). To calculate the overlapping area of two arbitrary camera sensors, the simple and intuitive method is to divide the overall network area into small grids, then check for each grid if its center whether in the FoV of a camera [5] or assuming the sensing area of sensor is composed of discrete points, then examine every point therein and determine if it also falls in others' sensing areas [3]. The time complexity of these two methods is decided by the size of the grid or the number of discrete points.…”

Visual correlation-based image gathering for wireless multimedia sensor networks

“…Our previous results [4] [11] show that the joint entropy for multiple images can be effectively estimated based on the visual correlation between cameras, and this correlation is given by a function of camera settings before the actual images are captured. Specifically, if two cameras and can both observe an area of interest , a spatial correlation coefficient , for the observations of at and is derived as…”

On full-view coverage in camera sensor networks

“…Such networks promise a wide range of applications in surveillance, traffic monitoring, habitat monitoring, health care and even online gaming [2]. Because of the huge potential in applications, camera sensor networks have drawn much attention in the past few years [18], [23].…”