Adaptive block compressed sensing - a technological analysis and survey on challenges, innovation directions and applica
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Adaptive block compressed sensing - a technological analysis and survey on challenges, innovation directions and applications R. Monika 1 & Dhanalakshmi Samiappan 1 & R. Kumar 1 Received: 26 August 2019 / Revised: 9 August 2020 / Accepted: 17 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In today’s digital world, data transmission and storage is becoming a massive problem. This is because the data produced by various sensors worldwide is outstripping the ability to store them. Pre-processing the entire data before transmission is the best solution for reducing the storage issue. ‘Compressed sensing’(CS) is a pre-processing technique that exploits the sparsity of the signal for sampling the data. Since most of the natural signals are sparse, CS allows sampling at a rate lesser than that required in Nyquist sampling theorem. However, in conventional CS, sampling is done for the entire image at once which increases processing time and reduces visual quality. In block compressed sensing (BCS), blocks of the images are processed simultaneously which increases processing speed and decreases the processing time. To improve the quality of the reconstructed signal, a variant of BCS, Adaptive block compressed sensing (ABCS) is used. This review paper studies the advantages, challenges and applications of applying ABCS for image compression. Keywords Compressed sensing . Block compressed sensing . Adaptive block compressed sensing . Saliency . Image perception . Texture contrast . Spatial entropy
1 Introduction Development of new sensors and sensing systems all over the world has led to the digital revolution in recent years. This advancement in digital technology has resulted in production of large amount of high-resolution data. However, It becomes a great challenge to compute and store
* Dhanalakshmi Samiappan [email protected]
1
Department of ECE, College of Engineering and Technology, Faculty of Engineering and Technology, SRM institute of Science and Technology, SRM Nagar, Kattankulathur 603203, Chengalpattu District, Tamilnadu, India
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such big volumes of data. Therefore we are in need of storage systems which provides huge capacity or ramp down the amount of data transmitted. Many approaches were developed and adopted to meet the storage demand. However, they are no longer sufficient as the data produced has experienced strong growth in recent years. Compression of data could serve the process. But conventional compression technique like JPEG have limitations in term of reconstructed image quality, data rate and compression performance with respect to CS [44]. Also, they follow Nyquist sampling theorem in which the sampling rate must be twice the bandwidth of the signal. But this is considered as an inefficient process in certain emerging applications as it ends up with too many samples. Thus Shannon’s theorem is not suitable for certain acquisition hardware as they cannot address storage issues [34]. The process can be made e
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