Adaptive image compression technique for wireless sensor networks

Nasri, Mohsen; Helali, Abdelhamid; Sghaier, Halim; Maâref, H.

doi:10.1016/j.compeleceng.2011.08.001

Cited by 23 publications

(11 citation statements)

References 19 publications

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“…In this study, we assume that the method 1 technique is applied to the first E transform levels out of the ( p − 1) total transform levels Nasri et al (2011). This is because the advantage of skipping high-pass coefficients is more significant at lower transform levels.…”

Section: Impact Of the Dwt On Computational Energymentioning

confidence: 98%

Images compression techniques for wireless sensor network applications

Nasri¹,

Helali

Sghaier

et al. 2014

Int J Speech Technol

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Adaptive compression for images transmission in resource-constrained multi-hop wireless network applications is considered. In this strategy, development of an energy efficient image compression scheme is proposed as a means to overcome the computation and/or energy limitation of individual nodes. It has the additional benefit of extending the "life" of individual node by saving its energy power. Two methods for energy efficient image compression are proposed and investigated with respect to energy consumption and image quality. Simulation results show that the proposed scheme prolongs the system lifetime and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses.

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Section: Impact Of the Dwt On Computational Energymentioning

confidence: 98%

Images compression techniques for wireless sensor network applications

Nasri¹,

Helali

Sghaier

et al. 2014

Int J Speech Technol

Self Cite

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“…In contrast with modern DNN-based approaches, application of well-known waveletbased image compression in WSNs is studied in Nasri, Helali, Sghaier, & Maaref, 2011. Their approach is based on the use of wavelet image transform and distributed image compression by sharing the processing of tasks to extend the overall lifetime of the network.…”

Section: Related Workmentioning

confidence: 99%

Image compression in resource-constrained eye tracking devices

Morozkin

Swynghedauw²,

Trocan

2019

Journal of Information and Telecommunication

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Resource-constrained embedded devices, operating with images, are becoming increasingly common. Examples include remote low-power smart sensors, wireless sensor networks, autonomous cameras, eye tracking devices, etc. The principal requirements of such devices are the operation in real time, low power, low heat as well as low MIPS. These requirements can be fulfilled with the use of approximated version of original image processing algorithms. The EyeDee™ embedded eye tracking solution (developed by SuriCog) is the world's first innovative solution using the eye as a real-time mobile digital cursor, while maintaining full mobility. Being an example of resourceconstrained embedded device, the system consists of a wearable device (Weetsy™ frame) capturing images on the human's eye and an embedded pre-processing device (Weetsy™ preprocessing board) sending these eye images over a transmission medium (wire/wireless transmission) to a remote processing unit for the further gaze reconstruction. This paper is aimed at introducing image compression approaches in the resourceconstrained devices in general and some of their implementation in the Weetsy™ pre-processing board in particular.

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“…Satellite and medical imagery are often high-resolution and at times require the original data to be protected from any loss. Different types of images such as grayscale, multi-spectral images, hyperspectral images, electrocardiogram (EEG), and Magnetic Resonance Imaging (MRI) have been processed to achieve lossless compression [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression

Waleed

Khan

et al. 2020

Applied Sciences

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Reversible Colour Transforms (RCTs) in conjunction with Bi-level Burrows–Wheeler Compression Algorithm (BBWCA) allows for high-level lossless image compression as demonstrated in this study. The RCTs transformation results in exceedingly coordinated image information among the neighbouring pixels as compared to the RGB colour space. This aids the Burrows–Wheeler Transform (BWT) based compression scheme and achieves compression ratios of high degree at the subsequent steps of the program. Validation has been done by comparing the proposed scheme across a range of benchmarks schemes and the performance of the proposed scheme is above par the other schemes. The proposed compression outperforms the techniques exclusively developed for 2-D electrocardiogram (EEG), RASTER map and Color Filter Array (CFA) image compression. The proposed system shows no dependency over parameters like image size, its type or the medium in which it is captured. A comprehensive analysis of the proposed scheme concludes that it achieves a significant increase in compression and depicts comparable complexity similar to the various benchmark schemes.

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Adaptive image compression technique for wireless sensor networks

Cited by 23 publications

References 19 publications

Images compression techniques for wireless sensor network applications

Images compression techniques for wireless sensor network applications

Image compression in resource-constrained eye tracking devices

On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression

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