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

Lu, Qin; Luo, Wenjian; Wang, Jidong; Chen, Bin

doi:10.1016/j.comnet.2008.05.006

Cited by 73 publications

(48 citation statements)

References 17 publications

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“…The work in [85] is inspired by the concept of parallel distributed computing theory. A distributed lapped biorthogonal transform-(LBT-) based image compression scheme is proposed for VSN.…”

Section: Distributed Source Coding Paradigmmentioning

confidence: 99%

A Survey of Image Compression Algorithms for Visual Sensor Networks

Mammeri

Hadjou

Khoumsi

2012

ISRN Sensor Networks

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With the advent of visual sensor networks (VSNs), energy-aware compression algorithms have gained wide attention. That is, new strategies and mechanisms for power-efficient image compression algorithms are developed, since the application of the conventional methods is not always energy beneficial. In this paper, we provide a survey of image compression algorithms for visual sensor networks, ranging from the conventional standards such as JPEG and JPEG2000 to a new compression method, for example, compressive sensing. We provide the advantages and shortcomings of the application of these algorithms in VSN, a literature review of their application in VSN, as well as an open research issue for each compression standard/method. Moreover, factors influencing the design of compression algorithms in the context of VSN are presented. We conclude by some guidelines which concern the design of a compression method for VSN.

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Section: Distributed Source Coding Paradigmmentioning

confidence: 99%

A Survey of Image Compression Algorithms for Visual Sensor Networks

Mammeri

Hadjou

Khoumsi

2012

ISRN Sensor Networks

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“…Some compression algorithms have been designed for WSNs [7], which include coding algorithm by ordering, pipelined in-network compression, low-complexity video compression, and distributed compression. To decrease the hardware cost and energy consumption, Lu et al [8] proposed the lowcomplexity and energy efficient image compression scheme, which reduced the computational complexity and required memory. A hardware solution for userdriven and packet loss tolerant image compression was presented and evaluated [9], which was designed to enable low power image compression and communication over wireless camera sensor networks.…”

Section: Related Workmentioning

confidence: 99%

Image processing and transmission scheme based on generalized Gaussian mixture with opportunistic networking for wireless sensor networks

Hao

2015

J Wireless Com Network

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For satisfying the quality of service (QoS) requirements and image denoising services in wireless sensor network (WSN) applications, based on opportunistic networking technology and generalized Gaussian mixture algorithm, an adaptive image processing and transmission scheme is proposed in this paper. According to the real-time state record matrix, the multi-objective optimization scheme with equalizer coefficients and the opportunistic cooperative scheme in view of energy and computing ability are studied, respectively. Then, the generalized Gaussian mixture algorithm is used to reduce the image data and eliminate the noise interference from the WSN environment. Finally, Simulation results show that the proposed scheme has better QoS support capability results such as reliability, real-time performance, and energy efficiency, as well as the image decoding accuracy including peak signal to noise ratio.

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“…A low-complexity and resource efficient image compression technique [11], for still images, reduces the processing and memory requirements. In the proposed approach, lapped biorthogonal transform (LBT) is used instead of discrete wavelet transform (DWT) or discrete cosine transform (DCT) to reduce the computational costs of about half of the computational cost by binary CDF9/7 wavelet.…”

Section: Related Workmentioning

confidence: 99%

“…We used the energy model in [11], where Pre energy for 1D preprocessing, DCT energy for DCT, and encode encoding energy add to the total LBT-based compression computational energy cp , as given in cp = 2 ( pre + DCT ) + encode .…”

Section: Energy Consumption Modelmentioning

confidence: 99%

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Power Efficient Clustering for Wireless Multimedia Sensor Network

Ikram

Jung

et al. 2014

International Journal of Distributed Sensor Networks

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The availability of inexpensive hardware such as CMOS cameras and microphones has fostered the development of wireless multimedia sensor networks (WMSNs). In WMSNs, wirelessly interconnected devices enable ubiquitously retrieving multimedia contents such as video and audio streams, and still images along with scalar data from surroundings for wide range of applications are constrained by processing, memory, and power resources. Image compression via low-complexity and resource efficient transforms has been addressed by several researchers to prolong network lifetime where energy conservation is achieved through sharing computational load among sensor nodes and by adjusting the transmission ranges of camera nodes. However, those schemes are not adaptive to the presence and changes of energy level of computational sensor nodes and to the amount of computational load. We propose a resource and energy efficient distributed image compression algorithm that dynamically configures according to the energy levels and the forwarding strategy that is based on the entropy of the image. The simulation results show that our adaptive distributed image compression scheme significantly prolongs the network lifetime and improves the network utilization efficiency, while maintaining adequate image quality.

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

Cited by 73 publications

References 17 publications

A Survey of Image Compression Algorithms for Visual Sensor Networks

A Survey of Image Compression Algorithms for Visual Sensor Networks

Image processing and transmission scheme based on generalized Gaussian mixture with opportunistic networking for wireless sensor networks

Power Efficient Clustering for Wireless Multimedia Sensor Network

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