An Effectual Video Compression Scheme for WVSNs Based on Block Compressive Sensing

Priya, G. L.; Ghosh, Debashis

doi:10.1109/tnse.2023.3325279

Cited by 3 publications

(1 citation statement)

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“…The principle of CS involves capturing a compressed version of a signal, enabling efficient data acquisition and transmission. As the Figure 1 shows, in the field of image processing, numerous works have been proposed in each of various domains, exploring innovative techniques and algorithms to harness the potential of compressive imaging [3][4][5][6][7][8][9][10][11], efficient communication systems [12][13][14][15][16][17][18][19][20], pattern recognition [21][22][23][24][25][26][27][28][29], and video processing tasks [30][31][32][33][34][35][36][37][38]. The versatility and effectiveness of CS make it a compelling area of study with broad implications across different fields of signal processing and information retrieval.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing in Image/Video Compression: Sampling, Coding, Reconstruction, and Codec Optimization

Zhou,

Yang

2024

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Compressive Sensing (CS) has emerged as a transformative technique in image compression, offering innovative solutions to challenges in efficient signal representation and acquisition. This paper provides a comprehensive exploration of the key components within the domain of CS applied to image and video compression. We delve into the fundamental principles of CS, highlighting its ability to efficiently capture and represent sparse signals. The sampling strategies employed in image compression applications are examined, emphasizing the role of CS in optimizing the acquisition of visual data. The measurement coding techniques leveraging the sparsity of signals are discussed, showcasing their impact on reducing data redundancy and storage requirements. Reconstruction algorithms play a pivotal role in CS, and this article reviews state-of-the-art methods, ensuring a high-fidelity reconstruction of visual information. Additionally, we explore the intricate optimization between the CS encoder and decoder, shedding light on advancements that enhance the efficiency and performance of compression techniques in different scenarios. Through a comprehensive analysis of these components, this review aims to provide a holistic understanding of the applications, challenges, and potential optimizations in employing CS for image and video compression tasks.

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