Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison

Abstract

There is continuous intensive research on image compression techniques in wireless sensor networks (WSNs) in the literature. Some of the image compression techniques in WSNs that exist in the literature include discrete cosine transform (DCT), discrete waveform transforms (DWT), set partitioning in a hierarchical tree (SPIHT), and embedded zero tree wavelet (EZW) coding. Research on image compression in WSNs is necessitated by the need to improve the energy efficiency of sensor nodes and WSNs' lifetimes without compromising the quality of the reconstructed data. Several approaches have been developed centered around image compression and other factors in trying to limit the energy consumption of sensor nodes. Most of these approaches do not provide the error-bound mechanism that balances the rate of compression and distortion of the reconstructed image. Therefore, in this paper, a review and analysis of image compression techniques and approaches in WSNs are conducted. Available image compression approaches in WSNs in literature were then classified according to the image compression technique adopted, and their strengths and weaknesses were highlighted. In addition, a rate-distortion balanced data compression algorithm with error bound mechanism based on artificial neural networks (ANN) in the form of an autoencoder (AE) was coded and simulated in MATLAB before being evaluated and compared to the conventional approaches. The experimental results show that the simulated algorithm has less root mean square error (RMSE) and a higher coefficient of determination (𝑅 2 ) values on variable compression ratios as compared to the Principal Component Analysis (PCA), Discrete Cosine Transform, and Fast Fourier Transform (FFT) when using the Grand-St-Bernard metrological dataset. Furthermore, it presented less RMSE, and higher compression ratio values compared to the Lightweight Temporal Compression (LTC) algorithm on variable error bounds when using the LUCE metrological dataset. Therefore, it was found that the simulated algorithm presents better compression fidelity as compared to the conventional approaches without an error-bound mechanism. Moreover, the algorithm analyzed presents a significant approach to balancing the compression ratio and reconstructed data quality through its error-bound mechanism.

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“…In MIMO wireless sensor networks, similar to traditional wireless sensor networks, each node typically possesses only one antenna, with nodes distributed across the network. To achieve higher spatial diversity gain, also referred to as cooperative diversity gain, these dispersed nodes collaborate to form a virtual antenna array [1,3]. The advantages of MIMO stem from its ability to boost throughput, extend coverage, and enhance capacity in a cost-effective manner.…”

“…Multiple-input-multiple-output (MIMO), or multiple antenna communication, has become increasingly significant in wireless systems in recent years. MIMO technology enhances the performance of Wireless Sensor Networks (WSNs) by leveraging multiple antennas at both the transmitter and receiver ends, thereby improving data throughput, enhancing reliability, and expanding the coverage of wireless communication in WSNs [1].…”

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression

“…On a daily basis, vision sensors capture billions of images, which are compressed with an image codec before they are stored or transferred. In fact, image compression plays the role of a fundamental tool which makes it possible to store and share an extensive amount of digital data, such as images and videos [ 3 ]. No doubt, image compression is a useful tool, however, while reconstructing images, lossy compression standards may cause some distortions in images that the human eye can detect while comparing the reconstructed images to the originals [ 4 ].…”

Integration of lightweight cryptography and watermarking with compression for high speed and reliable communication of digital images in IoT

“…In actuality, image compression is the process of reducing the size of an image in bytes without affecting its quality. So far, many compression methods have been proposed to solve various problems in digital imaging, which can be categorized into two main categories; lossy compression methods and lossless compression methods [13]. In lossy compression, a part of the original image data is always removed, but not enough to degrade the overall image.…”