Very Low-Memory Wavelet Compression Architecture Using Strip-Based Processing for Implementation in Wireless Sensor Networks

  • LiWern Chew1Email author,

    Affiliated with

    • WaiChong Chia1,

      Affiliated with

      • Li-minn Ang1 and

        Affiliated with

        • KahPhooi Seng1

          Affiliated with

          EURASIP Journal on Embedded Systems20092009:479281

          DOI: 10.1155/2009/479281

          Received: 4 March 2009

          Accepted: 9 September 2009

          Published: 13 December 2009

          Abstract

          This paper presents a very low-memory wavelet compression architecture for implementation in severely constrained hardware environments such as wireless sensor networks (WSNs). The approach employs a strip-based processing technique where an image is partitioned into strips and each strip is encoded separately. To further reduce the memory requirements, the wavelet compression uses a modified set-partitioning in hierarchical trees (SPIHT) algorithm based on a degree-0 zerotree coding scheme to give high compression performance without the need for adaptive arithmetic coding which would require additional storage for multiple coding tables. A new one-dimension (1D) addressing method is proposed to store the wavelet coefficients into the strip buffer for ease of coding. A softcore microprocessor-based hardware implementation on a field programmable gate array (FPGA) is presented for verifying the strip-based wavelet compression architecture and software simulations are presented to verify the performance of the degree-0 zerotree coding scheme.

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          Authors’ Affiliations

          (1)
          Department of Electrical and Electronic Engineering, The University of Nottingham

          Copyright

          © LiWern Chew et al. 2009

          This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.