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Related Experiment Videos

Hierarchical dynamic range coding of wavelet subbands for fast and efficient image decompression.

Yushin Cho1, William A Pearlman

  • 1Sony Electronics, Inc., San Jose, CA 95112, USA. cho.yushin@gmail.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 11, 2007
PubMed
Summary
This summary is machine-generated.

A new image coding algorithm, Progressive Resolution Coding (PROGRES), offers significantly faster decoding speeds than SPIHT. PROGRES achieves this by predicting wavelet subband ranges, maintaining coding efficiency without quality scalability.

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Area of Science:

  • Digital image processing
  • Wavelet transforms
  • Image compression

Background:

  • Scalable image coding is crucial for efficient transmission and storage.
  • Existing methods like SPIHT (Set Partitioning In Hierarchical Trees) offer good compression but can be computationally intensive for decoding.
  • There is a need for faster decoding algorithms without sacrificing compression efficiency.

Purpose of the Study:

  • To propose a novel image coding algorithm, Progressive Resolution Coding (PROGRES), optimized for high-speed resolution scalable decoding.
  • To investigate the performance of PROGRES in terms of decoding speed and coding efficiency compared to existing methods.
  • To explore the extensibility of PROGRES for random access decoding.

Main Methods:

  • PROGRES algorithm designed based on prediction of decaying dynamic ranges of wavelet subbands.
  • Bypasses bit-plane coding and complex list processing found in SPIHT.
  • Algorithm implemented and tested for both 2-D and 3-D wavelet subbands.

Main Results:

  • PROGRES decoding speeds are 4x faster for 2-D and 9x faster for 3-D compared to uncoded SPIHT, with similar decoded quality.
  • Coding efficiency is comparable to uncoded SPIHT, with identical bitstreams in the lossless case.
  • Demonstrates suitability for very large scale image encoding and decoding due to higher speed gains on larger images.

Conclusions:

  • PROGRES provides a significant speed improvement for scalable image decoding.
  • The algorithm maintains high coding efficiency and is easily extensible for random access.
  • PROGRES is a viable alternative for applications requiring fast decoding of wavelet-based image compression.