Elsevier

Computer Networks

Volume 52, Issue 13, 17 September 2008, Pages 2594-2603
Computer Networks

Low-complexity and energy efficient image compression scheme for wireless sensor networks

https://doi.org/10.1016/j.comnet.2008.05.006Get rights and content

Abstract

Currently most energy-constrained wireless sensor networks are designed with the object of minimizing the communication power at the cost of more computation. To achieve high compression efficiency, the main image compression algorithms used in wireless sensor networks are the high-complexity, state-of-the-art image compression standards, such as JPEG2000. These algorithms require complex hardware and make the energy consumption for computation comparable to communication energy dissipation. To reduce the hardware cost and the energy consumption of the sensor network, a low-complexity and energy efficient image compression scheme is proposed. The compression algorithm in the proposed scheme greatly lowers the computational complexity and reduces the required memory, while it still achieves required PSNR. The proposed implementation scheme of the image compression algorithm overcomes the computation and energy limitation of individual nodes by sharing the processing of tasks. And, it applies transmission range adjustment to save communication energy dissipation. Performance of the proposed scheme is investigated with respect to image quality and energy consumption. Simulation results show that it greatly prolongs the lifetime of the network under a specific image quality requirement.

Introduction

Many of the potential WSN applications such as target tracking, process control, source localization, discovering and following rare animal species, controlling the vehicle traffic on highways and railways necessitate efficient image communication in sensor networks. Since in these image-based applications, resources-constrained sensor nodes need to capture, compress and transmit a large amount of data, processing and communication efficiency of the compression algorithms is clearly a design constraint, which needs to be carefully addressed [1].

Recently a number of research efforts are under way to address the issue of image compression and transmission in sensor networks. Pradhan et al. proposed a distributed coding framework to realize the coding gain of correlated data from the Slepian–Wolf coding theorem in information theory [2]. Wagner et al. proposed another distributed image compression scheme [3] by sending the low-resolution overlapped areas to the receiver and using super-resolution recovery techniques to reconstruct them. Min Wu et al. proposed a novel collaborative image transmission scheme [4]. This scheme firstly employs a shape matching method along the route to find out the maximal overlap between images. Then the original image and the difference between another image and it are coded to transmit instead of transmitting two individual images independently. These works are designed with the object of minimizing the communication power at the cost of more computation. Except the distributed image compression for multiple correlated images based on the Slepian–Wolf coding theorem, few schemes consider the computational complexity of the image compression algorithms. The high-complexity state-of-the-art image compression standards, such as JPEG 2000, are still the main methods they used. However, these image compression algorithms are not suitable for resource-constrained wireless sensor networks because they require complex hardware and make the energy consumption for computation comparable to communication energy dissipation. Therefore, a low-complexity and high efficient image compression algorithm should be designed in order to reduce the hardware cost and the energy consumption of the sensor network.

To prolong the lifetime of the network, the implementation scheme of image compression algorithm should also be considered in the design. Traditionally, source coding is implemented at the source in order to reduce the number of bits transmitted thus reducing the communication energy dissipation. However, this approach is not always energy efficient for image-based applications. Centralized image compression which is used at the source nodes may sometimes limit the lifetime of the network.

In this paper, we present a low-complexity and energy efficient image compression scheme that is suitable for resource-constrained wireless sensor networks. The key features of this proposed scheme are:

  • Lapped biorthogonal transform (LBT) is used in image compression instead of discrete cosine transform (DCT) or discrete wavelet transform (DWT). Compared with the DCT-based methods, the proposed algorithm improves coding efficiency by taking into account inter-block spatial correlation and solves the problem of blocking artifacts. Compared with the DWT-based ones, the proposed algorithm greatly lowers the complexity of computation and reduces the required memory, while it still achieves required peak signal-to-noise ratio (PSNR). What is more, it is very suitable for distributed implementation in the sensor network.

  • Golomb + Multiple Quantization (MQ) coders are used in image compression instead of Huffman coding or arithmetic coding, so the computational complexity is significantly reduced. Further, the memory requirement is minimized because the proposed algorithm doses not require any statistical table or list when coding.

  • A distributed implementation scheme of the LBT-based image compression algorithm is proposed based on a clustering architecture. It overcomes the computation and energy limitation of individual nodes by sharing the processing of tasks. Both computational and communication energy consumption are considered. This greatly prolongs the lifetime of the wireless sensor network under a specific image quality requirement.

The rest of the paper is organized as follows. In Section 2, we briefly review related work. The algorithm of LBT and its corresponding encoding method is introduced in Section 3. Section 4 proposes the distributed implementation scheme of the LBT-based image compression algorithm. Simulations of proposed compression algorithm and implementation scheme are presented in Section 5. We conclude the paper in Section 6.

Section snippets

Related work

To our knowledge, low-complexity and energy efficient compression schemes for single image in wireless sensor networks have not been studied in the literature. However, our work has been inspired by a variety of related research efforts. We describe some of the ideas and basic concepts below.

Nowadays transform-based methods are still the most popular lossy image compression methods which mainly involve DCT-based methods and DWT-based ones. DCT-based methods have many fast algorithms with

Image compression algorithm based on LBT

In WSNs, we need an image compression method with low complexity as well as good image quality. Lapped biorthogonal transform (LBT) is a good candidate for this requirement. In this section, we present a lifting-based fast binary algorithm of LBT and a low-complexity zerotree coding method.

Image compression scheme

The goal of presenting a low-complexity and low-memory image compression algorithm based on LBT is to realize the image compression scheme in resource-constrained WSNs. We now propose a distributed implementation scheme of this image compression algorithm.

Performance evaluation

This section compares the performance of the proposed image compression algorithm with other image compression algorithms with respect to two metrics: image quality and the network lifetime.

Conclusions

We have studied the problem of image compression algorithm and its implementation scheme in WSNs. The design and evaluation of a low complexity and energy efficient image compression scheme is presented. A LBT-based low complexity and low memory image compression algorithm is used. It greatly reduces the hardware cost and the energy consumption of the sensor network. To prolong the lifetime of the network, both computational and communication energy consumption are considered in the

Acknowledgement

This work is partially supported by the 863-National High-Tech Research and Development Plan of China with Grant No. 2006AA701121.

Qin Lu received the B.S. and M.S. degrees in Instrumentation Science from the College of Mechatronics Engineering and Automation, National University of Defense Technology, China, in 2002 and 2004, respectively. She is now a Ph.D. student at the Department of Instrument Science and Technology in NUDT. Her major research interests include energy-efficient signal processing algorithms and low power DSPs for wireless systems.

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    Qin Lu received the B.S. and M.S. degrees in Instrumentation Science from the College of Mechatronics Engineering and Automation, National University of Defense Technology, China, in 2002 and 2004, respectively. She is now a Ph.D. student at the Department of Instrument Science and Technology in NUDT. Her major research interests include energy-efficient signal processing algorithms and low power DSPs for wireless systems.

    Wusheng Luo received the M.S. and Ph.D. degrees in Instrumentation Science from National University of Defense Technology, China, in 1997 and 2001, respectively. He is currently an assistant professor at the College of Mechatronics Engineering and Automation in NUDT. His research interests focus on signal processing, instrumentation, and measurement techniques.

    Jidong Wang received the M.S. degree in Mechanical and Electronic Engineering from National University of Defense Technology, China in 2003. Since 2004, he has been engaged in instrumentation, measurement, and sensing techniques. He is currently working toward the Ph.D. degree at the Department of Instrument Science and Technology in NUDT.

    Bo Chen received the B.S. and M.S. degrees in Applying Mathematica from the College of Science, National University of Defense Technology, China, in 2002 and 2004, respectively. He is now a Ph.D. student at the Department of Mathematic and System Science in NUDT. His research interests lie in the areas of multiscale analysis and image processing, specifically remote sensing image compression.

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