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Updated: Jul 2, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Image coding using dual-tree discrete wavelet transform.

Jingyu Yang1, Yao Wang, Wenli Xu

  • 1Department of Automation, Tsinghua University, Beijing, China. yangjy03@mails.tsinghua.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 15, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel image coding scheme using anisotropic dual-tree discrete wavelet packets (ADDWP) and TCE coding. The ADDWP-TCE method significantly outperforms JPEG2000, offering improved visual quality and coding efficiency.

Related Experiment Videos

Last Updated: Jul 2, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Area of Science:

  • Image processing
  • Signal processing
  • Computer vision

Background:

  • Directional and redundant transforms like 2-D dual-tree discrete wavelet transform (DDWT) offer advantages in image coding.
  • Existing methods for sparsifying DDWT coefficients and coding DDWT coefficients have varying efficiencies and complexities.
  • Directionality in wavelet transforms can lead to visually superior reconstructed images.

Purpose of the Study:

  • To evaluate and compare different methods for sparsifying DDWT coefficients.
  • To analyze dependencies among DDWT coefficients for improved coding.
  • To assess the performance of various subband coding methods for DDWT coefficients.
  • To introduce and evaluate an enhanced anisotropic dual-tree discrete wavelet packets (ADDWP) scheme for image coding.

Main Methods:

  • Comparison of sparsifying methods: matching pursuit, basis pursuit, and noise shaping.
  • Analysis of interscale, intersubband, and intrasubband dependencies of DDWT coefficients.
  • Evaluation of subband coders: SPIHT, EBCOT, and TCE for DDWT coefficients.
  • Development and testing of the DDWT-TCE and ADDWP-TCE image coding schemes.

Main Results:

  • Noise shaping demonstrated the best nonlinear approximation efficiency with minimal computational cost for sparsifying DDWT coefficients.
  • The TCE subband coder exhibited superior performance for coding DDWT coefficients.
  • The DDWT-TCE scheme outperformed JPEG2000 at low bit rates (up to 0.70 dB) and was comparable at high bit rates.
  • The enhanced ADDWP-TCE scheme provided significant improvements, outperforming DDWT-TCE by up to 1.47 dB and JPEG2000 by up to 2.00 dB.
  • Reconstructed images from DDWT-TCE and ADDWP-TCE schemes showed enhanced visual appeal due to wavelet directionality.

Conclusions:

  • Noise shaping and TCE coding are effective for DDWT-based image compression.
  • The proposed ADDWP-TCE scheme offers superior coding efficiency and visual quality compared to JPEG2000 and other directional filter bank-based methods.
  • The directionality inherent in the ADDWP transform contributes to visually appealing image reconstructions.