There are many shortcomings in traditional JPEG2000 image compression transmission algorithms in the wireless networks, such as, large energy consumption of resources in the network, large stress and energy consumption of the compressed image acquisition node and long transmission time, the image compression algorithm based on singular value decomposition is proposed. Block adaptive compression algorithm based on singular value decomposition is used in image compression processing, through the

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... nalysis of singular value decomposition and network energy consumption model. Meanwhile, the task of the network data processing and long-distance data transmission is assigned to different roles node to complete to balance the distribution of the network energy consumption. Finally, detailed experimental simulation have done, and the experimental results show that compared with the traditional compression algorithm (JPEG2000 image compression), the image compression algorithm based on singular value decomposition greatly alleviates the network camera key node energy consumption, and improves the speed of multimedia data transmission in wireless multimedia sensor network, and quality. complexity of the wavelet transform and the excessive energy consumption of wireless transceiver, the method is not ideal to apply inWMSNs . Wang Pu et al. put forward image compression coding agreement framework based on the information entropy difference measurement scheme and distributed clusters to maximize the overall compression of WMSNs visual information [11] . Nasri M put forward image compression mechanism, which focused on achieving a best balance between the image reconstruction quality and node energy consumption [12] [13] [14] . Lu Q etc based on camera node and ordinary node in the network situation, put forward using the camera node to acquire images and the around ordinary nodes clustering for collaborative coding transmission scheme to balance the network energy consumption and prolong network life time. Huu P N et al. put forward a node role transformation plan to prolong network life to avoid the uneven problems of the node energy consumption when use the Lu Q's overlap transform methods to compress images, the collaborative node is too little [14] [15] [16] [17] [18] . In image compression method, the image compression method based on singular value decomposition (SVD) has received extensive attention of the researchers. The method is suitable for block. Based on this, this paper puts forward a WMSNs image compression collaboration mechanism, which is based on singular value decomposition. We block the image obtained by the camera and adopt to the adaptive singular value decomposition method to improve the compression of each block. Meanwhile, the camera node of image compression and transmission tasks effectively decompose into multiple ordinary nodes surrounding the camera node by using the collaboration features of WMSNs nodes, so as to realize the goal of energy saving and maximize the network life cycle. This paper mainly in the following aspects as the development and innovative work: (a) On the basis of analyzing the singular value decomposition method and the model of network energy and the transmission image compression algorithm based on singular value decomposition in the compression and transmission, wireless multimedia sensor networks was studied based on singular value decomposition of the block image adaptive compression algorithms to balance the network energy consumption. According to the different roles of the camera node and ordinary node, they will complete the image acquisition, compression and transmission work, and the camera node is responsible for collecting the image information, and then send image block to ordinary nodes in the cluster; the ordinary nodes in the clusters will adaptive compress the block image and send the data to cluster head node; and then, the cluster head node will send the compressed data to the base station. The image compression algorithm based on singular value decomposition greatly alleviates the network camera key node energy consumption, and improves the speed of multimedia data transmission in wireless multimedia sensor network, and quality. (b) To further verify the correctness and validity of compression and transmission of the transmission image compression algorithm based on singular value decomposition in wireless multimedia sensor networks, we carried a detailed experimental simulation and compared with JPEG2000 image compression algorithm. The experimental results show that compared with the traditional compression algorithms, it greatly reduces the energy consumption of network camera key node, and can effectively balance the network energy consumption and prolong the network life cycle. Proposed Method From the perspective of linear algebra, a digital image can be seen as a nonnegative matrix. Given the basic principle and related properties of singular value decomposition, performance on the digital image watermarking has several notable characteristics: (a) the embedded 196 Copyright ⓒ 2015 SERSC common node and the cluster head node carry on the division of labor, and they collaborate for image compression and transmission. Compared Figure 5 (a) and (b) it can be seen, we can see that the average energy consumption of the latter camera node is reduced by nearly an order of magnitude than the former. As the energy consumption of nodes in the network has a strong ability of balance, lifetime of the network will be greatly prolonged. Aiming at the feature of basic balance of data processing and transmission energy consumption in WMSNs application, this paper starts from collaboration features of the network node, adopts SVD-based block adaptive compression algorithms for image compression disposal, and assigns the tasks of data processing and long-distance data transfer in network to different roles to complete them in order to balance the network energy distribution. Simulating results show that the SVD-based image distributed collaboration compression mechanism greatly eases energy consumption of the key node of the network or the camera node, and it can effectively balance the network energy consumption and prolong the network life-cycle.