Study On Image Data Compression Processing In Wireless Multimedia Sensor Network

WMSNs is on the basis of WSNs with introducing audio, video, image, sound,large-capacity information. It’s widely used in environmental monitoring, battlefieldsurveillance, traffic monitoring, ect. As energy of nodes, processing power, storage capacityof WMSNs in very limited, efficient use of energy is the primary objection of WMSNs. Thecompression and transmission of image is critical. This paper introduces the concept andcharacteristics of wireless multimedia sensor networks, and analyzes the main challenges inwireless multimedia sensor networks faces, discusses the concrete problems in imageprocessing of WMSNs.At present, the main problems in WMSNs is energy consumption problem, How tomake the energy limited sensor nodes processing and transmission the rich imageinformation data. The research to solve the problem of energy consumption of networknodes focus on two aspects: First make some processing on image information collectedafter transmission; Second how to transfer the processed date effectively. Imagecompression has solve the problem of transmission for large amount data, but with thecompression ratio increases damage the image quality, So if you want get highercompression ratio is very difficult to meet certain engineering application.According to the related problems of wireless multimedia sensor network in practicalapplication, this paper presents a lossy-lossless image compression algorithm based on theinterest extraction. The algorithm separates the interest part and the not interest part of theimage according to the practical application of Engineering, interested part adopts thelossless compression get high quality image, not interested part use lossy compression toachieve higher compression ratio. Combined with distributed processing, the imagecompression and processing process is assigned to multiple sensor nodes distributedprocessing. In the premise of meet project demand the algorithm enhances the networktransmission efficiency, balances the energy consumption of the network and improves the network life cycle.