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A low memory zerotree coding for arbitrarily shaped objects.

Chorng-Yann Su1, Bing-Fei Wu

  • 1Dept. of Ind. Educ., Nat. Taiwan Normal Univ., Hsinchu, Taiwan. scy@cc.ntnu.edu.tw

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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A new low memory zerotree coder (LMZC) significantly reduces working memory for image compression compared to the set partitioning in hierarchical trees (SPIHT) algorithm. This memory-efficient approach is ideal for hardware applications with limited resources.

Area of Science:

  • Digital image processing
  • Data compression algorithms
  • Computer hardware engineering

Background:

  • The Set Partitioning in Hierarchical Trees (SPIHT) algorithm is an efficient zerotree coding technique for image compression.
  • SPIHT's high working memory requirement poses a challenge for hardware implementation.

Purpose of the Study:

  • To introduce a Low Memory Zerotree Coder (LMZC) with reduced working memory needs.
  • To enhance image compression efficiency for memory-constrained applications.

Main Methods:

  • LMZC abandons lists, utilizes a novel tree structure, and integrates sorting and refinement passes.
  • Key techniques include recursive programming and a Top-Bit Scheme (TBS) for coding status.
  • Shape-adaptive discrete wavelet transforms and compact coefficient emplacement are employed.

Related Experiment Videos

Main Results:

  • LMZC requires significantly less working memory than SPIHT, saving at least 5.3 MBytes for a 768x512 color image.
  • Execution time increases slightly, and Peak Signal-to-Noise Ratio (PSNR) values see minor reductions.
  • The algorithm efficiently handles "don't care" nodes in the wavelet tree.

Conclusions:

  • LMZC offers a promising solution for memory-limited image compression applications.
  • The reduction in memory footprint makes it suitable for hardware realization.
  • LMZC balances memory efficiency with acceptable performance trade-offs.