Abstract
Wireless Visual Sensor Network (WVSN) has become a valuable tool in addressing the evolving needs of modern monitoring systems. Encoding in WVSNs is a multifaceted process that involves compressing visual data, optimizing energy consumption, ensuring error resilience, and adapting to various network and application requirements. The associated lightweight encoders and the demand for less storage space make block compressive sensing (BCS) techniques suitable for WVSN applications where energy, bandwidth, and storage resources are limited. Based on the number of visual perspectives or camera angles available within a network for data capture, there are two primary congurations: monoview and multiview. This paper provides a comprehensive survey of dierent BCS-based encoding schemes used for data-gathering in both monoview and multiview scenarios within WVSNs. A comparative study of these algorithms based on compression level, computational complexity, relative gain in encoder energy, and reconstruction quality is performed. A BCS-based joint encoding scheme for multiview conguration that ensures a relatively high compression level is also proposed in this paper.
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