A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EZW-Based Scheme for VSN. <s> Embedded zerotree wavelet (EZW) coding, introduced by Shapiro (see IEEE Trans. Signal Processing, vol.41, no.12, p.3445, 1993), is a very effective and computationally simple technique for image compression. We offer an alternative explanation of the principles of its operation, so that the reasons for its excellent performance can be better understood. These principles are partial ordering by magnitude with a set partitioning sorting algorithm, ordered bit plane transmission, and exploitation of self-similarity across different scales of an image wavelet transform. Moreover, we present a new and different implementation based on set partitioning in hierarchical trees (SPIHT), which provides even better performance than our previously reported extension of EZW that surpassed the performance of the original EZW. The image coding results, calculated from actual file sizes and images reconstructed by the decoding algorithm, are either comparable to or surpass previous results obtained through much more sophisticated and computationally complex methods. In addition, the new coding and decoding procedures are extremely fast, and they can be made even faster, with only small loss in performance, by omitting entropy coding of the bit stream by the arithmetic code. <s> BIB001 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EZW-Based Scheme for VSN. <s> Wireless sensor networks (WSNs) have the potential to assist advanced target tracking applications. The major challenge related to the design of such networks is to cope with the energy and computational limitations that characterize sensor nodes. To address this problem, we propose a hybrid architecture that integrates two sensor categories. The first performs basic detection and tracking functions while the second supports complex tasks such as imaging and broadband communication via a satellite network. Moreover, we develop a technique that allows vision sensors adapting the rate of the exchanged data according to the target activity in the monitored zone. Finally, a tracking approach taking into consideration the error on local sensor position measurements is presented. <s> BIB002
The unique research work adopting EZW as a compression tool for VSN is that one suggested in BIB002 . The authors in BIB002 suggest a multimodal sensor network architecture using acoustic, electromagnetic, and visual sensors, along with a satellite communication backbone. Based on the collaborative effort of this architecture, the target position is recognized, and its fine details are acquired using visual sensors. For this purpose, EZW coding algorithm is adapted to VSN requirements. This is performed by introducing spatial information about target activity. The adapted EZW provides high-resolution data for the regions where one or more intrusions have been detected and low-resolution data for the remaining regions. This scheme allows saving in bandwidth, power, and storage resources. The adoption of the EZW as a compression tool in VSN can be beneficial in terms of power consumption. This is due to the relatively simple complexity of its encoder and its progressive paradigm. An open research work should be the adaptation of the EZW algorithm to the power-constrained VSN. This is can be performed by minimizing the number of passes to minimize the memory required to store the significant wavelet coefficients found at each pass. BIB001 is an improvement of EZW algorithm. By adopting set partitioning algorithm and exploring self-similarity across different scales in an image wavelet transform, SPIHT algorithm reaches high compression performance. Unlike EZW, SPIHT maintains three linked lists and four sets of wavelet coordinates, which are deeply explained in BIB001 . With SPIHT, the image is first wavelet decomposed into a series of wavelet coefficients. Those coefficients are then grouped into sets known as spatial orientation trees. After that, the coefficients in each spatial orientation tree are encoded progressively from the most significant bit planes to the least significant bit planes, starting with the coefficients with the highest magnitude. As with EZW, the SPIHT algorithm involves two coding passes: the sorting pass and the refinement pass. The sorting pass looks for zerotrees and sorts significant and insignificant coefficients with respect to a given threshold. And the refinement pass sends the precision bits of the significant coefficients. After one sorting pass and one refinement pass, which can be considered as one scan pass, the threshold T is halved, and the coding process is repeated until the expected bit rate is achieved.