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Related Experiment Videos

Hyperspectral image compression using entropy-constrained predictive trellis coded quantization.

G P Abousleman1, M W Marcellin, B R Hunt

  • 1Dept. of Electr. and Comput. Eng., Arizona Univ., Tucson, AZ.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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A new entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme improves autoregressive source encoding. This advanced method offers superior mean squared error performance and effective hyperspectral image compression.

Area of Science:

  • Digital Signal Processing
  • Image Compression
  • Information Theory

Background:

  • Autoregressive sources are common in signal and image processing.
  • Efficient compression techniques are crucial for managing large datasets, especially hyperspectral images.
  • Entropy-constrained quantization aims to minimize distortion while controlling the information rate.

Purpose of the Study:

  • To introduce a novel training-sequence-based entropy-constrained predictive trellis coded quantization (ECPTCQ) scheme.
  • To evaluate the performance of ECPTCQ for encoding autoregressive sources, specifically a first-order Gauss-Markov source.
  • To develop and assess a hyperspectral image compression system employing the ECPTCQ scheme.

Main Methods:

  • Developed a training-sequence-based ECPTCQ scheme.

Related Experiment Videos

  • Analyzed the mean squared error (MSE) performance for a first-order Gauss-Markov source.
  • Implemented ECPTCQ within a hyperspectral image compression system.

    • Main Results:

    • The eight-state ECPTCQ system demonstrated up to 1.0 dB improvement in MSE over entropy-constrained differential pulse code modulation (ECDPCM) for a first-order Gauss-Markov source.
    • Hyperspectral image sequences compressed using ECPTCQ at 0.125 bits/pixel/band maintained an average peak signal-to-noise ratio (PSNR) exceeding 43 dB across spectral bands.

    Conclusions:

    • The proposed ECPTCQ scheme provides significant performance gains for autoregressive source encoding.
    • ECPTCQ is effective for hyperspectral image compression, achieving high PSNR at low bit rates.
    • This method offers a promising approach for efficient data compression in spectral imaging applications.