Energy Driven Selection and Hardware Implementation of Bi-Level Image Compression

Abstract

Wireless Vision Sensor Nodes are considered to have smaller resources and are expected to have a longer lifetime based on the available limited energy. A wireless Vision Sensor Node (VSN) is often characterized to consume more energy in communication as compared to processing. The communication energy can be reduced by reducing the amount of transmission data with the help of a suitable compression scheme. This work investigates bi-level compression schemes including G4, G3, JBIG2, Rectangular, GZIP, GZIP_Pack and JPEG-LS on a hardware platform. The investigation results show that GZIP_pack, G4 and JBIG2 schemes are suitable for a hardware implemented VSN. JBIG2 offers up to a 43 percent reduction in overall energy consumption as compared to G4 and GZIP_pack for complex images. However, JBIG2 has higher resource requirement and implementation complexity. The difference in overall energy consumption is smaller for smooth images. Depending on the application requirement, the exclusion of a header can reduce the energy consumption by approximately 1 to 33 percent.