A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT-Based Schemes for VSN. <s> We propose an energy efficient JPEG 2000 image transmission system over point-to-point wireless sensor networks. The objective is to minimize the overall processing and transmission energy consumption with the expected end-to-end QoS guarantee, which is achieved by jointly adjusting the source coding schemes, channel coding rates, and transmitter power levels in an optimal way. The advantages of the proposed system lie in three aspects: adaptivity, optimality, and low complexity. Based on the characteristics of the image content, the estimated channel conditions, and the distortion constraint, the proposed low-complexity joint source channel coding and power control algorithm adjusts the coding and transmission strategies adaptively, which can approximate the optimal solution with a tight bound. <s> BIB001 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT-Based Schemes for VSN. <s> We consider transmitting images in a multihop wireless network with the minimal total power consumption while satisfying an end-to-end image quality constraint. Contrary to popular belief, we show that maximum compression before transmission does not always provide minimal energy consumption, especially in the case of dense sensor networks with complex signal processing algorithms. We formulate the minimal energy transmission problem as an optimization problem and present a heuristic algorithm for it. The proposed algorithm selects the optimal image compression parameters to minimize total energy dissipation given the network conditions and image quality constraints. Simulation results show up to 80% reduction in the total power consumption achieved by using the proposed adaptive algorithm compared to nonadaptive algorithms. <s> BIB002 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT-Based Schemes for VSN. <s> We propose an embedded, block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect to thresholds that are integer powers of two. It exploits two fundamental characteristics of an image transform-the well-defined hierarchical structure, and energy clustering in frequency and in space. The two partition strategies allow for versatile and efficient coding of several image transform structures, including dyadic, blocks inside subbands, wavelet packets, and discrete cosine transform (DCT). We describe the use of this coding algorithm in several implementations, including reversible (lossless) coding and its adaptation for color images, and show extensive comparisons with other state-of-the-art coders, such as set partitioning in hierarchical trees (SPIHT) and JPEG2000. We conclude that this algorithm, in addition to being very flexible, retains all the desirable features of these algorithms and is highly competitive to them in compression efficiency. <s> BIB003 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT-Based Schemes for VSN. <s> In traditional WSNs, sensor nodes usually compress the data before sending them to the cluster head. However, this strategy may not be suitable for image transmission. If the image compression task rests mostly on camera-equipped node, it will deplete its energy quickly. To prolong the network lifetime, a novel network architecture is proposed in this paper. Except the camera-equipped nodes, all the normal sensor nodes are organized into clusters. The camera-equipped node does not join the cluster nearby directly. It forms its own cluster and transmits the raw image to the members in the cluster. These members in the camera-node cluster also belong to the normal-node clusters. They share the task of image compression and take the responsibility to transmit the compressed image to the cluster head. Simulation results show that the proposed architecture can greatly prolong the lifetime of the network many times in the case of the sensor nodes deployed densely. <s> BIB004
In this section, we review the main schemes adopting EBCOT (or JPEG2000) for compression purpose in VSN. We start by the architecture suggested in BIB004 which releases the visual sensors from the burden compression process, to prolong the network lifetime. Except the camera sensor, all data sensors are organized into clusters. The visual sensor does not join the cluster directly. Rather, it forms its own cluster and sends the target image to the cluster members. These members in the VS cluster, which belong to the data sensor clusters, share the task of image compression and transmission to the cluster head. Both computational and communication energy consumptions are considered in this architecture. For compression purposes, the authors in BIB004 use the standard JPEG2000, which increases rapidly the energy dissipation. By simulation, the authors show that this architecture can prolong the lifetime of the network. The authors in BIB001 propose an energy efficient JPEG2000 scheme for image processing and transmission, given the expected end-to-end distortion constraint. In the suggested scheme, called joint source channel coding and power control (JSCCPC), the input image is firstly encoded as a scalable bitstream in an optimal number of layers. Based on the three following factors: the estimated channel condition, the characteristics of the image content, and the endto-end distortion constraint, the suggested scheme determines adaptively the number of transmitted layers. Moreover, the JSCCPC unit adjusts the source coding rate, the source level error resilience scheme, the channel coding rate, and the transmitter power level for each layer. This approach extensively explores the multiresolution nature of bitstreams; however, the unequal importance between structure information and magnitude information is not fully identified. The authors show by simulations that up to 45% less energy consumption could be achieved under relatively severe channel conditions. Another energy-aware scheme for efficient image compression for VSN is that one suggested in BIB002 , where the authors formulate this challenging task as an optimization problem. They use JPEG2000 standard on a StrongArm SA-1000 processor. For a given image quality requirement and network conditions, the authors investigate a heuristic algorithm to select the optimal parameters of a wavelet-based coder, while minimizing the total energy dissipation. Results indicate that large fractions of the total energy are spent on computation due to the high complexity of JPEG2000. From [45], we can conclude that maximal compression before transmission may not always entail minimal energy consumption. However, their approaches mainly focus on power efficient techniques for individual components and cannot provide a favorable energy performance trade-off in the case of WSN. Carrying out EBCOT or JPEG2000 in camera sensors may not always be the smart choice, since its implementation complexity induces high power consumption, where it is implemented (e.g., in VS), and possibly shrinks the network connectivity. Moreover, when combined with DWT stage (as with JPEG2000), more power will be dissipated due to the fact that DWT phase power consumption is significant and represents the second source consumption of an EBCOT-DWT compression scheme after Tier-1's EBCOT. An eventual open research work should be the adaptation of EBCOT to VSN constraints, taking advantage of some potential solutions to alleviate the workload and the complexity of the EBCOT algorithm. BIB003 , where the authors suggest a compression algorithm that makes use of sets of pixels in the form of block when spanning wavelet subbands, instead of using trees as with EZW or SPIHT. SPECK algorithm starts by performing an appropriate subband transformation (usually, the DWT) on the input image, which allows its decomposition into a series of coefficients. After that, two phases are repeated recursively until the expected bit rate is achieved: sorting pass and refinement pass phase. Recall that SPECK necessitates three phases: initialization, sorting pass, and refinement pass phase. Unlike EZW, SPECK maintains two linked lists: list of insignificant sets (LISs) and list of significant pixels (LSPs).