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Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding.

Manoranjan Paul1, Rui Xiao1, Junbin Gao2

  • 1CM3 Research Unit, School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, 2795, Australia.

Plos One
|October 4, 2016
PubMed
Summary
This summary is machine-generated.

Hyperspectral (HS) image compression requires a shift from pixel intensity to residual-based coding. A novel framework uses Reflectance Prediction Modelling (RPM) within High Efficiency Video Coding (HEVC) for superior HS image compression performance.

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Area of Science:

  • Image Processing
  • Computer Vision
  • Data Compression

Background:

  • Hyperspectral (HS) images contain significantly more data than traditional images.
  • Existing compression methods based on original pixel intensity are inefficient for HS data.
  • HS images possess unique spectral and shape characteristics requiring specialized coding approaches.

Purpose of the Study:

  • To propose a novel coding framework for efficient HS image compression.
  • To adapt video coding techniques for the unique properties of HS imagery.
  • To improve the rate-distortion performance of HS image compression.

Main Methods:

  • A novel framework using Reflectance Prediction Modelling (RPM) integrated with High Efficiency Video Coding (HEVC).
  • Modeling pixel vector distribution and correlation across spectral bands.
  • Estimating spectral bands using Gaussian mixture-based modeling for prediction.
  • Treating each spectral band as an individual video frame for coding.

Main Results:

  • The proposed RPM-based HEVC framework effectively predicts spectral band distributions.
  • Experimental results demonstrate superior rate-distortion performance compared to mainstream HS encoders.
  • The method was validated using diverse HS datasets across different wavelength ranges.

Conclusions:

  • The proposed coding framework offers a fundamental shift for effective HS image compression.
  • RPM within HEVC significantly enhances compression efficiency by exploiting spatial-spectral redundancy.
  • This approach provides a promising solution for transmitting and storing large HS image datasets.