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Related Experiment Videos

A jointly optimized subband coder.

F Kossentini1, W C Chung, M T Smith

  • 1Digital Signal Process. Lab., Georgia Inst. of Technol., Atlanta, GA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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This study introduces a novel joint optimization approach for subband coding, enhancing image compression efficiency. This method minimizes distortion by optimizing quantizers and entropy coders together, outperforming traditional separate coding techniques.

Area of Science:

  • Signal Processing
  • Image Compression
  • Information Theory

Background:

  • Traditional subband coding partitions signals and codes them separately.
  • This approach often leads to suboptimal performance due to unexploited statistical dependencies.

Purpose of the Study:

  • To develop a more effective subband coding approach through joint optimization.
  • To minimize average distortion under rate constraints for improved image coding.

Main Methods:

  • Introduced a subband coder with jointly optimized multistage residual quantizers and entropy coders.
  • Applied the developed coder to image coding tasks.
  • Exploited statistical dependencies within and across subbands.

Main Results:

Related Experiment Videos

  • The jointly optimized coder demonstrated high performance in image compression.
  • Achieved a superior balance between reproduction quality, data rate, and computational complexity.
  • Effectively utilized multistage residual quantization and statistical modeling.

Conclusions:

  • Joint optimization of subband coders offers a significant improvement over mainstream approaches.
  • The proposed coder effectively leverages intra- and inter-subband statistical dependencies.
  • This method provides an attractive solution for efficient and high-quality image compression.