Evaluation of context models to code wavelet-transformed hyperspectral images

Francesc Aulí-Llinàs, Pablo Enfedaque, Joan Serra-Sagristà, Victor Sanchez

Research output: Contribution to journalLiterature reviewResearchpeer-review


Context modeling is key in wavelet-based image coding schemes to achieve competitive coding performance. Commonly, context models are devised for a particular coding system and are employed for many different types of images. The aim of this work is to evaluate the suitability of three well-known context models for coding hyperspectral images, without focusing on a particular wavelet-based coding system. To do so, an entropy-based measure defined using the mechanisms utilized by modern image codecs is employed. The experimental results assess the appropriateness of the context models considering different coding rates and transform strategies. They reveal that some widely-used context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based codecs for hyperspectral images.

Original languageAmerican English
Pages (from-to)4827-4831
Number of pages5
Journal2014 IEEE International Conference on Image Processing, ICIP 2014
Publication statusPublished - 28 Jan 2014


  • context models
  • Image coding
  • wavelet transform


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