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LLEC: An Image Coder with Low-Complexity and Low-Memory Requirement

  • Debin Zhao
  • Wen Gao
  • Shiguang Shan
  • Y. K. Chan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2195)

Abstract

A Low-complexity and Low-memory Entropy Coder (LLEC) for image compression is proposed in this paper. The two key elements in LLEC are zerotree coding and Golomb-Rice codes. Zerotree coding exploits the zerotree structure of transformed coefficients for higher compression efficiency. Golomb-Rice codes are used to code the remaining coefficients in a VLC/VLI manner for low complexity and low memory. The experimental results show that the compression efficiency of DCT- and DWT-based LLEC outperforms baseline JPEG and EZW at the given bit rates, respectively. When compared with SPIHT, LLEC is inferior by 0.3 dB on average for the tested images but superior in terms of computational complexity and memory requirement. In addition LLEC has other desirable features such as parallel processing support, ROI (Region Of Interest) coding and as a universal entropy coder for DCT and DWT.

Keywords

Discrete Cosine Transform Memory Requirement Discrete Cosine Transform Coefficient Quantization Step Arithmetic Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Debin Zhao
    • 1
  • Wen Gao
    • 1
    • 2
  • Shiguang Shan
    • 2
  • Y. K. Chan
    • 3
  1. 1.Department of Computer ScienceHarbin Institute of TechnologyHarbinChina
  2. 2.Institute of Computing TechnologyCASBeijingChina
  3. 3.Department of Computer ScienceCity University of Hong KongHong Kong

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