Efficient Multispectral Image Communication Using Compressed Sensing
DOI:
https://doi.org/10.13052/jmm1550-4646.2223Keywords:
Compressed sensing, image communication, Peak Signal to Noise Ratio, Multispectral imagesAbstract
This paper explores a joint method for efficient compression as well as the reliable transmission of images. In image transmission, low dormancy and very fast data transmission are the main requirements of modern wireless communications systems. Efficient and reliable image transmission through wireless networks need to handle several challenges like adverse wireless channel conditions, the need for high power consumption, managing high computational complexity, and low error resilience capability of image compression schemes. This paper deals with the compressed sensing approach in combination with orthogonal frequency division multiplexing (OFDM) to take care of the above challenges. There are three important steps in compressive sensing: sparse signal representation, measurement collection, and sparse recovery. In this process, a measurement matrix is utilized to sample those elements which are significant for accurately depicting the signal in the measurement step. So, the design of precise measurement matrices is crucial for compressive sensing. This paper proposes a Lanczos measurement matrix which significantly improves the quality of the reconstructed image with minimum data. At the same time, the use of OFDM handles multipath fading channels for reliable transmission of the image data. The simulation results also compare the performance of several measurement matrices in terms of image quality through peak signal-to-noise ratio (PSNR) value, the structural similarity index, and the Bit Error Rate (BER) for transmission performance after passing through Additive White Gaussian Noise (AWGN) as well as multipath channels.
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