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Wireless Visual Sensor Networks (WVSN) is formed by deploying many Visual Sensor Nodes (VSNs) in the field.
Typical applications of WVSN include environmental monitoring, health care, industrial process monitoring,
stadium/airports monitoring for security reasons and many more. The energy budget in the outdoor applications of
WVSN is limited to the batteries and the frequent replacement of batteries is usually not desirable. So the processing as
well as the communication energy consumption of the VSN needs to be optimized in such a way that the network
remains functional for longer duration. The images captured by VSN contain huge amount of data and require efficient
computational resources for processing the images and wide communication bandwidth for the transmission of the
results. Image processing algorithms must be designed and developed in such a way that they are computationally less
complex and must provide high compression rate. For some applications of WVSN, the captured images can be
segmented into bi-level images and hence bi-level image coding methods will efficiently reduce the information amount
in these segmented images. But the compression rate of the bi-level image coding methods is limited by the underlined
compression algorithm. Hence there is a need for designing other intelligent and efficient algorithms which are
computationally less complex and provide better compression rate than that of bi-level image coding methods. Change
coding is one such algorithm which is computationally less complex (require only exclusive OR operations) and provide
better compression efficiency compared to image coding but it is effective for applications having slight changes
between adjacent frames of the video. The detection and coding of the Region of Interest (ROIs) in the change frame
efficiently reduce the information amount in the change frame. But, if the number of objects in the change frames is
higher than a certain level then the compression efficiency of both the change coding and ROI coding becomes worse
than that of image coding. This paper explores the compression efficiency of the Binary Video Codec (BVC) for the data
reduction in WVSN. We proposed to implement all the three compression techniques i.e. image coding, change coding
and ROI coding at the VSN and then select the smallest bit stream among the results of the three compression
techniques. In this way the compression performance of the BVC will never become worse than that of image coding.
We concluded that the compression efficiency of BVC is always better than that of change coding and is always better
than or equal that of ROI coding and image coding.
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