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Significance-linked connected component analysis for wavelet image coding.

B B Chai1, J Vass, X Zhuang

  • 1Sarnoff Corporation, Princeton, NJ 08543, USA. bchai@sarnoff.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 13, 2008
PubMed
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A new wavelet image coder, significance-linked connected component analysis (SLCCA), significantly improves compression performance over existing methods like EZW, MRWD, and SPIHT. This advanced technique offers faster encoding and decoding for efficient image compression.

Area of Science:

  • Computer Vision
  • Image Processing
  • Data Compression

Background:

  • Wavelet image coding relies heavily on data organization and representation.
  • Existing competitive wavelet coders include embedded zerotree wavelets (EZW), morphological representation of wavelet data (MRWD), and set partitioning in hierarchical trees (SPIHT).

Purpose of the Study:

  • To develop a novel wavelet image coder that enhances compression efficiency.
  • To improve upon existing methods by exploiting coefficient clustering and cross-subband dependencies.

Main Methods:

  • Developed significance-linked connected component analysis (SLCCA) for wavelet coefficients.
  • Extended the morphological representation of wavelet data (MRWD) approach.
  • Exploited within-subband clustering and cross-subband dependency of significant coefficients.

Related Experiment Videos

Main Results:

  • SLCCA demonstrated superior performance compared to EZW, MRWD, and SPIHT on natural and texture images.
  • Achieved higher Peak Signal-to-Noise Ratio (PSNR) values, e.g., 1.41 dB improvement over EZW for the Barbara image at 0.25 b/pixel.
  • Showed excellent results for texture images, outperforming SPIHT by 0.16 dB-0.63 dB at 0.40 b/pixel.

Conclusions:

  • SLCCA offers a significant advancement in wavelet image coding.
  • The method achieves high compression performance without complex bit allocation, ensuring fast encoding and decoding.
  • SLCCA is particularly effective for compressing images with substantial texture content.