Bi-Level Video Codec for Machine Vision Embedded Applications

Abstract

Wireless Visual Sensor Networks (WVSN) are feasible today due to the advancement in many fields of electronics such as Complementary Metal Oxide Semiconductor (CMOS) cameras, low power electronics, distributed computing and radio transceivers. The energy budget in WVSN is limited due to the small form factor of the Visual Sensor Nodes (VSNs) and the wireless nature of the application. The images captured by VSN contain huge amount of data which leads to high communication energy consumptions. Hence there is a need for designing efficient algorithms which are computationally less complex and provide high compression ratio. The change coding and Region of Interest (ROIs) coding are the options for data reduction of the VSN. But, for higher number of objects in the images, the compression efficiency of both the change coding and ROI coding becomes worse than that of image coding. This paper explores the compression efficiency of the Bi-Level Video Codec (BVC) for several representative machine vision applications. We proposed to implement image coding, change coding and ROI coding at the VSN and to select the smallest bit stream among the three. Results show that the compression performance of the BVC for such applications is always better than that of change coding and ROI coding. Index Terms-Wireless sensor networks, low power electronics, embedded computing, image communication. I. INTRODUCTION The Wireless Visual Sensor Network (WVSN) is composed of many Visual Sensor Nodes (VSNs). Each VSN consist of an image sensor for capturing images of the field of interest, microcontroller or Field Programmable gate Arrays (FPGA) for image processing, a radio transceiver for transmitting the results and energy resource for providing power to all the other components. The VSN is capable of performing complex image processing algorithms using limited energy resources due to the technological advancement in many fields of electronics such as Complementary Metal Oxide Semiconductor (CMOS) image sensors, wireless transceivers, low power computing platforms/embedded systems and distributed computing. Many authors in the literature e.g. [1]-[3] have focused on different implementation strategies for performing vision Manuscript

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