Image subset communication for resource-constrained applications in wireless sensor networks
Author(s) -
Sajid Nazir
Publication year - 2020
Publication title -
turkish journal of electrical engineering and computer sciences
Language(s) - English
Resource type - Journals
eISSN - 1303-6203
pISSN - 1300-0632
DOI - 10.3906/elk-2002-169
Subject(s) - computer science , jpeg , image compression , software deployment , wireless sensor network , base station , wireless , real time computing , field (mathematics) , data compression , image (mathematics) , image processing , computer network , artificial intelligence , telecommunications , mathematics , pure mathematics , operating system
JPEG is the most widely used image compression standard for sensing, medical, and security applications. JPEG provides a high degree of compression but field devices relying on battery power must further economize on data transmissions to prolong deployment duration with particular use cases in wireless sensor networks. Transmitting a subset of image data could potentially enhance the battery life of power-constrained devices and also meet the application requirements to identify the objects within an image. Depending on an application’s needs, after the first selected subset is received at the base station, further transmissions of the image data for successive refinements can also be requested. Needs for such progressive refinements exist in applications including tele-medicine, security, and surveillance, where an initial assessment could govern further exploration of only a small region. We propose a scheme for selecting minimum information for a coarser reconstruction by transmitting only the DC coefficients as the first or base layer. This initial layer of information could on request be augmented by transmitting either more data representing an entire image or a selected region of interest. We compare our results with those of other power economization and progressive communications techniques. The proposed scheme offers significant advantages for a range of application scenarios under resource constraints.
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